In [1]:
!pip install -U seaborn
Requirement already up-to-date: seaborn in /opt/conda/lib/python3.6/site-packages (0.9.0)
Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /opt/conda/lib/python3.6/site-packages (from seaborn) (1.12.1)
Requirement already satisfied, skipping upgrade: pandas>=0.15.2 in /opt/conda/lib/python3.6/site-packages (from seaborn) (0.23.3)
Requirement already satisfied, skipping upgrade: scipy>=0.14.0 in /opt/conda/lib/python3.6/site-packages (from seaborn) (0.19.1)
Requirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /opt/conda/lib/python3.6/site-packages (from seaborn) (2.1.0)
Requirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.15.2->seaborn) (2.6.1)
Requirement already satisfied, skipping upgrade: pytz>=2011k in /opt/conda/lib/python3.6/site-packages (from pandas>=0.15.2->seaborn) (2017.3)
Requirement already satisfied, skipping upgrade: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)
Requirement already satisfied, skipping upgrade: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib>=1.4.3->seaborn) (0.10.0)
Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.2.0)
In [2]:
## Importing the necessary packages required for the project
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import requests
import json
import tweepy
from timeit import default_timer as timer
import glob
import re
import seaborn as sns

Gathering

In [3]:
## Reading the twitter_ archive-enhnced csv file
df1=pd.read_csv("twitter-archive-enhanced.csv")
In [4]:
df1.head()
Out[4]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name doggo floofer pupper puppo
0 892420643555336193 NaN NaN 2017-08-01 16:23:56 +0000 <a href="http://twitter.com/download/iphone" r... This is Phineas. He's a mystical boy. Only eve... NaN NaN NaN https://twitter.com/dog_rates/status/892420643... 13 10 Phineas None None None None
1 892177421306343426 NaN NaN 2017-08-01 00:17:27 +0000 <a href="http://twitter.com/download/iphone" r... This is Tilly. She's just checking pup on you.... NaN NaN NaN https://twitter.com/dog_rates/status/892177421... 13 10 Tilly None None None None
2 891815181378084864 NaN NaN 2017-07-31 00:18:03 +0000 <a href="http://twitter.com/download/iphone" r... This is Archie. He is a rare Norwegian Pouncin... NaN NaN NaN https://twitter.com/dog_rates/status/891815181... 12 10 Archie None None None None
3 891689557279858688 NaN NaN 2017-07-30 15:58:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Darla. She commenced a snooze mid meal... NaN NaN NaN https://twitter.com/dog_rates/status/891689557... 13 10 Darla None None None None
4 891327558926688256 NaN NaN 2017-07-29 16:00:24 +0000 <a href="http://twitter.com/download/iphone" r... This is Franklin. He would like you to stop ca... NaN NaN NaN https://twitter.com/dog_rates/status/891327558... 12 10 Franklin None None None None
In [5]:
## downloading the html filw with requests library
url = ' https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
response = requests.get(url)
In [6]:
## Saving the HTML to a file
with open ("image-predictions.tsv" , mode= 'wb') as file :
    file.write (response.content)
In [7]:
## Reading the tsv file
imagedf1 = pd.read_csv('image-predictions.tsv',sep ='\t')
In [8]:
imagedf1.head()
Out[8]:
tweet_id jpg_url img_num p1 p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog
0 666020888022790149 https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg 1 Welsh_springer_spaniel 0.465074 True collie 0.156665 True Shetland_sheepdog 0.061428 True
1 666029285002620928 https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg 1 redbone 0.506826 True miniature_pinscher 0.074192 True Rhodesian_ridgeback 0.072010 True
2 666033412701032449 https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg 1 German_shepherd 0.596461 True malinois 0.138584 True bloodhound 0.116197 True
3 666044226329800704 https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg 1 Rhodesian_ridgeback 0.408143 True redbone 0.360687 True miniature_pinscher 0.222752 True
4 666049248165822465 https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg 1 miniature_pinscher 0.560311 True Rottweiler 0.243682 True Doberman 0.154629 True
Query Twitter API for each tweet in the Twitter archive and save JSON in a text file
These are hidden to comply with Twitter's API terms and conditions

consumer_key = 'Your credentials' consumer_secret = 'Your credentials' access_token = 'Your Credentials' access_secret = 'Your Credentials'

auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret)

Twitter api code for accessing the data from the Twitter API
Twitter api code will extract information based upon the tweet_id given in the df1
df1.tweet_id.values creates an array of all the id values in tweet_id

api = tweepy.API(auth, wait_on_rate_limit=True) tweet_ids = df1.tweet_id.values len(tweet_ids)

Query Twitter's API for JSON data for each tweet ID in the Twitter archive

count = 0 fails_dict = {} start = timer()

Save each tweet's returned JSON as a new line in a .txt file

with open('tweet_json.txt', 'w') as outfile:

# This loop will likely take 20-30 minutes to run because of Twitter's rate limit
for tweet_id in tweet_ids:
    count += 1
    print(str(count) + ": " + str(tweet_id))
    try:
        tweet = api.get_status(tweet_id, tweet_mode='extended')
        print("Success")
        json.dump(tweet._json, outfile)
        outfile.write('\n')
    except tweepy.TweepError as e:
        print("Fail")
        fails_dict[tweet_id] = e
        pass

end = timer() print(end - start) print(fails_dict)

In [9]:
## Loading the text file
## We are creating an empty list, and opening the the text file with json structure and reading line by line for extracting the data
tweet_api =[]
with open('tweet_json.txt','r') as f:
    for line in f:
        tweet_api.append(json.loads(line))
In [10]:
## Displaying the tweet_api list
tweet_api
Out[10]:
[{'created_at': 'Tue Aug 01 16:23:56 +0000 2017',
  'id': 892420643555336193,
  'id_str': '892420643555336193',
  'full_text': "This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU",
  'truncated': False,
  'display_text_range': [0, 85],
  'entities': {'hashtags': [],
   'symbols': [],
   'user_mentions': [],
   'urls': [],
   'media': [{'id': 892420639486877696,
     'id_str': '892420639486877696',
     'indices': [86, 109],
     'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',
     'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',
     'url': 'https://t.co/MgUWQ76dJU',
     'display_url': 'pic.twitter.com/MgUWQ76dJU',
     'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',
     'type': 'photo',
     'sizes': {'large': {'w': 540, 'h': 528, 'resize': 'fit'},
      'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
      'small': {'w': 540, 'h': 528, 'resize': 'fit'},
      'medium': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},
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     'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',
     'url': 'https://t.co/MgUWQ76dJU',
     'display_url': 'pic.twitter.com/MgUWQ76dJU',
     'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',
     'type': 'photo',
     'sizes': {'large': {'w': 540, 'h': 528, 'resize': 'fit'},
      'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
      'small': {'w': 540, 'h': 528, 'resize': 'fit'},
      'medium': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},
  'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>',
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': None,
  'in_reply_to_user_id_str': None,
  'in_reply_to_screen_name': None,
  'user': {'id': 4196983835,
   'id_str': '4196983835',
   'name': 'WeRateDogs™ (author)',
   'screen_name': 'dog_rates',
   'location': 'DM YOUR DOGS, WE WILL RATE',
   'description': '#1 Source for Professional Dog Ratings | STORE: @ShopWeRateDogs | IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame | Business: dogratingtwitter@gmail.com',
   'url': 'https://t.co/N7sNNHAEXS',
   'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS',
       'expanded_url': 'http://weratedogs.com',
       'display_url': 'weratedogs.com',
       'indices': [0, 23]}]},
    'description': {'urls': []}},
   'protected': False,
   'followers_count': 3200889,
   'friends_count': 104,
   'listed_count': 2784,
   'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
   'favourites_count': 114031,
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   'time_zone': None,
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   'notifications': False,
   'translator_type': 'none'},
  'geo': None,
  'coordinates': None,
  'place': None,
  'contributors': None,
  'is_quote_status': False,
  'retweet_count': 8853,
  'favorite_count': 39467,
  'favorited': False,
  'retweeted': False,
  'possibly_sensitive': False,
  'possibly_sensitive_appealable': False,
  'lang': 'en'},
 {'created_at': 'Tue Aug 01 00:17:27 +0000 2017',
  'id': 892177421306343426,
  'id_str': '892177421306343426',
  'full_text': "This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV",
  'truncated': False,
  'display_text_range': [0, 138],
  'entities': {'hashtags': [],
   'symbols': [],
   'user_mentions': [],
   'urls': [],
   'media': [{'id': 892177413194625024,
     'id_str': '892177413194625024',
     'indices': [139, 162],
     'media_url': 'http://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg',
     'media_url_https': 'https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg',
     'url': 'https://t.co/0Xxu71qeIV',
     'display_url': 'pic.twitter.com/0Xxu71qeIV',
     'expanded_url': 'https://twitter.com/dog_rates/status/892177421306343426/photo/1',
     'type': 'photo',
     'sizes': {'large': {'w': 1407, 'h': 1600, 'resize': 'fit'},
      'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
      'small': {'w': 598, 'h': 680, 'resize': 'fit'},
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     'id_str': '892177413194625024',
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     'media_url_https': 'https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg',
     'url': 'https://t.co/0Xxu71qeIV',
     'display_url': 'pic.twitter.com/0Xxu71qeIV',
     'expanded_url': 'https://twitter.com/dog_rates/status/892177421306343426/photo/1',
     'type': 'photo',
     'sizes': {'large': {'w': 1407, 'h': 1600, 'resize': 'fit'},
      'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
      'small': {'w': 598, 'h': 680, 'resize': 'fit'},
      'medium': {'w': 1055, 'h': 1200, 'resize': 'fit'}}}]},
  'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>',
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': None,
  'in_reply_to_user_id_str': None,
  'in_reply_to_screen_name': None,
  'user': {'id': 4196983835,
   'id_str': '4196983835',
   'name': 'WeRateDogs™ (author)',
   'screen_name': 'dog_rates',
   'location': 'DM YOUR DOGS, WE WILL RATE',
   'description': '#1 Source for Professional Dog Ratings | STORE: @ShopWeRateDogs | IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame | Business: dogratingtwitter@gmail.com',
   'url': 'https://t.co/N7sNNHAEXS',
   'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS',
       'expanded_url': 'http://weratedogs.com',
       'display_url': 'weratedogs.com',
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    'description': {'urls': []}},
   'protected': False,
   'followers_count': 3200889,
   'friends_count': 104,
   'listed_count': 2784,
   'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
   'favourites_count': 114031,
   'utc_offset': None,
   'time_zone': None,
   'geo_enabled': True,
   'verified': True,
   'statuses_count': 5288,
   'lang': 'en',
   'contributors_enabled': False,
   'is_translator': False,
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   'has_extended_profile': True,
   'default_profile': False,
   'default_profile_image': False,
   'following': True,
   'follow_request_sent': False,
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   'translator_type': 'none'},
  'geo': None,
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  'place': None,
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  'retweet_count': 6514,
  'favorite_count': 33819,
  'favorited': False,
  'retweeted': False,
  'possibly_sensitive': False,
  'possibly_sensitive_appealable': False,
  'lang': 'en'},
 {'created_at': 'Mon Jul 31 00:18:03 +0000 2017',
  'id': 891815181378084864,
  'id_str': '891815181378084864',
  'full_text': 'This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB',
  'truncated': False,
  'display_text_range': [0, 121],
  'entities': {'hashtags': [],
   'symbols': [],
   'user_mentions': [],
   'urls': [],
   'media': [{'id': 891815175371796480,
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     'indices': [122, 145],
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     'media_url_https': 'https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg',
     'url': 'https://t.co/wUnZnhtVJB',
     'display_url': 'pic.twitter.com/wUnZnhtVJB',
     'expanded_url': 'https://twitter.com/dog_rates/status/891815181378084864/photo/1',
     'type': 'photo',
     'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'},
      'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
      'large': {'w': 1201, 'h': 1600, 'resize': 'fit'},
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     'display_url': 'pic.twitter.com/wUnZnhtVJB',
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     'type': 'photo',
     'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'},
      'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
      'large': {'w': 1201, 'h': 1600, 'resize': 'fit'},
      'small': {'w': 510, 'h': 680, 'resize': 'fit'}}}]},
  'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>',
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': None,
  'in_reply_to_user_id_str': None,
  'in_reply_to_screen_name': None,
  'user': {'id': 4196983835,
   'id_str': '4196983835',
   'name': 'WeRateDogs™ (author)',
   'screen_name': 'dog_rates',
   'location': 'DM YOUR DOGS, WE WILL RATE',
   'description': '#1 Source for Professional Dog Ratings | STORE: @ShopWeRateDogs | IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame | Business: dogratingtwitter@gmail.com',
   'url': 'https://t.co/N7sNNHAEXS',
   'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS',
       'expanded_url': 'http://weratedogs.com',
       'display_url': 'weratedogs.com',
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    'description': {'urls': []}},
   'protected': False,
   'followers_count': 3200889,
   'friends_count': 104,
   'listed_count': 2784,
   'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
   'favourites_count': 114031,
   'utc_offset': None,
   'time_zone': None,
   'geo_enabled': True,
   'verified': True,
   'statuses_count': 5288,
   'lang': 'en',
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   'has_extended_profile': True,
   'default_profile': False,
   'default_profile_image': False,
   'following': True,
   'follow_request_sent': False,
   'notifications': False,
   'translator_type': 'none'},
  'geo': None,
  'coordinates': None,
  'place': None,
  'contributors': None,
  'is_quote_status': False,
  'retweet_count': 4328,
  'favorite_count': 25461,
  'favorited': False,
  'retweeted': False,
  'possibly_sensitive': False,
  'possibly_sensitive_appealable': False,
  'lang': 'en'},
 {'created_at': 'Sun Jul 30 15:58:51 +0000 2017',
  'id': 891689557279858688,
  'id_str': '891689557279858688',
  'full_text': 'This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ',
  'truncated': False,
  'display_text_range': [0, 79],
  'entities': {'hashtags': [],
   'symbols': [],
   'user_mentions': [],
   'urls': [],
   'media': [{'id': 891689552724799489,
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     'indices': [80, 103],
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     'media_url_https': 'https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg',
     'url': 'https://t.co/tD36da7qLQ',
     'display_url': 'pic.twitter.com/tD36da7qLQ',
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 {'created_at': 'Tue Jun 28 23:23:19 +0000 2016',
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   'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
   'favourites_count': 114031,
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   'verified': True,
   'statuses_count': 5288,
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  'geo': None,
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 ...]
In [11]:
##Converting the list into dataframe
tweets_api=pd.DataFrame(tweet_api)
In [12]:
tweets_api
Out[12]:
contributors coordinates created_at display_text_range entities extended_entities favorite_count favorited full_text geo ... possibly_sensitive_appealable quoted_status quoted_status_id quoted_status_id_str retweet_count retweeted retweeted_status source truncated user
0 None None Tue Aug 01 16:23:56 +0000 2017 [0, 85] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 892420639486877696, 'id_str'... 39467 False This is Phineas. He's a mystical boy. Only eve... None ... False NaN NaN NaN 8853 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
1 None None Tue Aug 01 00:17:27 +0000 2017 [0, 138] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 892177413194625024, 'id_str'... 33819 False This is Tilly. She's just checking pup on you.... None ... False NaN NaN NaN 6514 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2 None None Mon Jul 31 00:18:03 +0000 2017 [0, 121] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 891815175371796480, 'id_str'... 25461 False This is Archie. He is a rare Norwegian Pouncin... None ... False NaN NaN NaN 4328 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
3 None None Sun Jul 30 15:58:51 +0000 2017 [0, 79] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 891689552724799489, 'id_str'... 42908 False This is Darla. She commenced a snooze mid meal... None ... False NaN NaN NaN 8964 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
4 None None Sat Jul 29 16:00:24 +0000 2017 [0, 138] {'hashtags': [{'text': 'BarkWeek', 'indices': ... {'media': [{'id': 891327551943041024, 'id_str'... 41048 False This is Franklin. He would like you to stop ca... None ... False NaN NaN NaN 9774 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
5 None None Sat Jul 29 00:08:17 +0000 2017 [0, 138] {'hashtags': [{'text': 'BarkWeek', 'indices': ... {'media': [{'id': 891087942176911360, 'id_str'... 20562 False Here we have a majestic great white breaching ... None ... False NaN NaN NaN 3261 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
6 None None Fri Jul 28 16:27:12 +0000 2017 [0, 140] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 890971906207338496, 'id_str'... 12041 False Meet Jax. He enjoys ice cream so much he gets ... None ... False NaN NaN NaN 2158 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
7 None None Fri Jul 28 00:22:40 +0000 2017 [0, 118] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 890729118844600320, 'id_str'... 56848 False When you watch your owner call another dog a g... None ... False NaN NaN NaN 16716 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
8 None None Thu Jul 27 16:25:51 +0000 2017 [0, 122] {'hashtags': [{'text': 'BarkWeek', 'indices': ... {'media': [{'id': 890609177319665665, 'id_str'... 28226 False This is Zoey. She doesn't want to be one of th... None ... False NaN NaN NaN 4429 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
9 None None Wed Jul 26 15:59:51 +0000 2017 [0, 133] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 890240245463175168, 'id_str'... 32467 False This is Cassie. She is a college pup. Studying... None ... False NaN NaN NaN 7711 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
10 None None Wed Jul 26 00:31:25 +0000 2017 [0, 130] {'hashtags': [{'text': 'BarkWeek', 'indices': ... {'media': [{'id': 890006600089468928, 'id_str'... 31166 False This is Koda. He is a South Australian decksha... None ... False NaN NaN NaN 7624 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
11 None None Tue Jul 25 16:11:53 +0000 2017 [0, 107] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 889880888800096258, 'id_str'... 28268 False This is Bruno. He is a service shark. Only get... None ... False NaN NaN NaN 5156 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
12 None None Tue Jul 25 01:55:32 +0000 2017 [0, 106] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 889665366129029120, 'id_str'... 38818 False Here's a puppo that seems to be on the fence a... None ... False NaN NaN NaN 8538 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
13 None None Tue Jul 25 00:10:02 +0000 2017 [0, 91] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 889638825424826374, 'id_str'... 27672 False This is Ted. He does his best. Sometimes that'... None ... False NaN NaN NaN 4735 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
14 None None Mon Jul 24 17:02:04 +0000 2017 [0, 118] {'hashtags': [{'text': 'BarkWeek', 'indices': ... {'media': [{'id': 889531127467266049, 'id_str'... 15359 False This is Stuart. He's sporting his favorite fan... None ... False NaN NaN NaN 2321 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
15 None None Mon Jul 24 00:19:32 +0000 2017 [0, 138] {'hashtags': [{'text': 'BarkWeek', 'indices': ... {'media': [{'id': 889278779352338437, 'id_str'... 25652 False This is Oliver. You're witnessing one of his m... None ... False NaN NaN NaN 5637 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
16 None None Sun Jul 23 00:22:39 +0000 2017 [0, 86] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 888917229776945152, 'id_str'... 29611 False This is Jim. He found a fren. Taught him how t... None ... False NaN NaN NaN 4709 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
17 None None Sat Jul 22 16:56:37 +0000 2017 [0, 128] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 888804981515575296, 'id_str'... 26080 False This is Zeke. He has a new stick. Very proud o... None ... False NaN NaN NaN 4559 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
18 None None Sat Jul 22 00:23:06 +0000 2017 [0, 87] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 888554915546542081, 'id_str'... 20290 False This is Ralphus. He's powering up. Attempting ... None ... False NaN NaN NaN 3732 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
19 None None Thu Jul 20 16:49:33 +0000 2017 [0, 127] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 888078426338406400, 'id_str'... 22201 False This is Gerald. He was just told he didn't get... None ... False NaN NaN NaN 3653 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
20 None None Wed Jul 19 16:06:48 +0000 2017 [0, 127] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 887705281597243393, 'id_str'... 30779 False This is Jeffrey. He has a monopoly on the pool... None ... False NaN NaN NaN 5609 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
21 None None Wed Jul 19 03:39:09 +0000 2017 [0, 108] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 887517108413886465, 'id_str'... 46959 True I've yet to rate a Venezuelan Hover Wiener. Th... None ... False NaN NaN NaN 12082 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
22 None None Wed Jul 19 00:47:34 +0000 2017 [0, 99] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 887473949361045505, 'id_str'... 69871 False This is Canela. She attempted some fancy porch... None ... False NaN NaN NaN 18781 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
23 None None Tue Jul 18 16:08:03 +0000 2017 [0, 88] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 887343120832229379, 'id_str'... 34222 False You may not have known you needed to see this ... None ... False NaN NaN NaN 10737 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
24 None None Tue Jul 18 00:07:08 +0000 2017 [0, 129] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 887101385971384320, 'id_str'... 31061 False This... is a Jubilant Antarctic House Bear. We... None ... False NaN NaN NaN 6167 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
25 None None Mon Jul 17 16:17:36 +0000 2017 [0, 101] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 886983218871902208, 'id_str'... 35859 False This is Maya. She's very shy. Rarely leaves he... None ... False NaN NaN NaN 8084 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
26 None None Sun Jul 16 23:58:41 +0000 2017 [0, 121] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 886736868116754432, 'id_str'... 12306 False This is Mingus. He's a wonderful father to his... None ... False NaN NaN NaN 3443 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
27 None None Sun Jul 16 20:14:00 +0000 2017 [0, 71] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 886680331239161856, 'id_str'... 22798 False This is Derek. He's late for a dog meeting. 13... None ... False NaN NaN NaN 4610 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
28 None None Sat Jul 15 23:25:31 +0000 2017 [0, 131] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 886366138128449536, 'id_str'... 21524 False This is Roscoe. Another pupper fallen victim t... None ... False NaN NaN NaN 3316 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
29 None None Sat Jul 15 16:51:35 +0000 2017 [27, 105] {'hashtags': [], 'symbols': [], 'user_mentions... NaN 117 False @NonWhiteHat @MayhewMayhem omg hello tanner yo... None ... NaN NaN NaN NaN 4 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2324 None None Tue Nov 17 00:53:15 +0000 2015 [0, 138] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666418781095260160, 'id_str'... 129 False This is Walter. He is an Alaskan Terrapin. Lov... None ... False NaN NaN NaN 48 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2325 None None Tue Nov 17 00:24:19 +0000 2015 [0, 140] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666411498068123649, 'id_str'... 459 False This is quite the dog. Gets really excited whe... None ... False NaN NaN NaN 339 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2326 None None Mon Nov 16 23:23:41 +0000 2015 [0, 137] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666396240351993856, 'id_str'... 172 False Oh goodness. A super rare northeast Qdoba kang... None ... False NaN NaN NaN 92 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2327 None None Mon Nov 16 21:54:18 +0000 2015 [0, 81] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666373746337402880, 'id_str'... 194 False Those are sunglasses and a jean jacket. 11/10 ... None ... False NaN NaN NaN 100 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2328 None None Mon Nov 16 21:10:36 +0000 2015 [0, 140] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666362717482020864, 'id_str'... 804 False Unique dog here. Very small. Lives in containe... None ... False NaN NaN NaN 595 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2329 None None Mon Nov 16 20:32:58 +0000 2015 [0, 135] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666353280906170368, 'id_str'... 229 False Here we have a mixed Asiago from the Galápagos... None ... False NaN NaN NaN 77 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2330 None None Mon Nov 16 20:01:42 +0000 2015 [0, 112] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666345414279471104, 'id_str'... 307 False Look at this jokester thinking seat belt laws ... None ... False NaN NaN NaN 146 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2331 None None Mon Nov 16 19:31:45 +0000 2015 [0, 139] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666337857791987715, 'id_str'... 204 False This is an extremely rare horned Parthenon. No... None ... False NaN NaN NaN 96 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2332 None None Mon Nov 16 16:37:02 +0000 2015 [0, 138] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666293909010702337, 'id_str'... 522 False This is a funny dog. Weird toes. Won't come do... None ... False NaN NaN NaN 368 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2333 None None Mon Nov 16 16:11:11 +0000 2015 [0, 136] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666287399580733440, 'id_str'... 152 False This is an Albanian 3 1/2 legged Episcopalian... None ... False NaN NaN NaN 71 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2334 None None Mon Nov 16 15:14:19 +0000 2015 [0, 46] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666273081518768128, 'id_str'... 184 False Can take selfies 11/10 https://t.co/ws2AMaNwPW None ... False NaN NaN NaN 82 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2335 None None Mon Nov 16 14:57:41 +0000 2015 [0, 82] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666268904428277760, 'id_str'... 108 False Very concerned about fellow dog trapped in com... None ... False NaN NaN NaN 37 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2336 None None Mon Nov 16 04:02:55 +0000 2015 [0, 134] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666104129232740352, 'id_str'... 14765 False Not familiar with this breed. No tail (weird).... None ... False NaN NaN NaN 6871 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2337 None None Mon Nov 16 03:55:04 +0000 2015 [0, 128] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666102150364286977, 'id_str'... 81 False Oh my. Here you are seeing an Adobe Setter giv... None ... False NaN NaN NaN 16 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2338 None None Mon Nov 16 03:44:34 +0000 2015 [0, 140] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666099505364733952, 'id_str'... 164 False Can stand on stump for what seems like a while... None ... False NaN NaN NaN 73 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2339 None None Mon Nov 16 03:22:39 +0000 2015 [0, 132] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666093996847063040, 'id_str'... 169 False This appears to be a Mongolian Presbyterian mi... None ... False NaN NaN NaN 79 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2340 None None Mon Nov 16 02:38:37 +0000 2015 [0, 125] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666082912819875840, 'id_str'... 121 False Here we have a well-established sunblockerspan... None ... False NaN NaN NaN 47 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2341 None None Mon Nov 16 01:59:36 +0000 2015 [0, 137] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666073098362486784, 'id_str'... 335 False Let's hope this flight isn't Malaysian (lol). ... None ... False NaN NaN NaN 174 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2342 None None Mon Nov 16 01:52:02 +0000 2015 [0, 137] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666071190449033216, 'id_str'... 154 False Here we have a northern speckled Rhododendron.... None ... False NaN NaN NaN 67 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2343 None None Mon Nov 16 01:22:45 +0000 2015 [0, 107] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666063820255862784, 'id_str'... 496 False This is the happiest dog you will ever see. Ve... None ... False NaN NaN NaN 232 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2344 None None Mon Nov 16 01:01:59 +0000 2015 [0, 135] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666058597072306176, 'id_str'... 115 False Here is the Rand Paul of retrievers folks! He'... None ... False NaN NaN NaN 61 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2345 None None Mon Nov 16 00:55:59 +0000 2015 [0, 124] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666057085227016192, 'id_str'... 304 False My oh my. This is a rare blond Canadian terrie... None ... False NaN NaN NaN 146 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2346 None None Mon Nov 16 00:49:46 +0000 2015 [0, 140] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666055517517848576, 'id_str'... 448 False Here is a Siberian heavily armored polar bear ... None ... False NaN NaN NaN 261 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2347 None None Mon Nov 16 00:35:11 +0000 2015 [0, 138] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666051848592334848, 'id_str'... 1253 False This is an odd dog. Hard on the outside but lo... None ... False NaN NaN NaN 879 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2348 None None Mon Nov 16 00:30:50 +0000 2015 [0, 140] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666050754986266625, 'id_str'... 136 False This is a truly beautiful English Wilson Staff... None ... False NaN NaN NaN 60 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2349 None None Mon Nov 16 00:24:50 +0000 2015 [0, 120] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666049244999131136, 'id_str'... 111 False Here we have a 1949 1st generation vulpix. Enj... None ... False NaN NaN NaN 41 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2350 None None Mon Nov 16 00:04:52 +0000 2015 [0, 137] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666044217047650304, 'id_str'... 311 False This is a purebred Piers Morgan. Loves to Netf... None ... False NaN NaN NaN 147 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2351 None None Sun Nov 15 23:21:54 +0000 2015 [0, 130] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666033409081393153, 'id_str'... 128 False Here is a very happy pup. Big fan of well-main... None ... False NaN NaN NaN 47 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2352 None None Sun Nov 15 23:05:30 +0000 2015 [0, 139] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666029276303482880, 'id_str'... 132 False This is a western brown Mitsubishi terrier. Up... None ... False NaN NaN NaN 48 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...
2353 None None Sun Nov 15 22:32:08 +0000 2015 [0, 131] {'hashtags': [], 'symbols': [], 'user_mentions... {'media': [{'id': 666020881337073664, 'id_str'... 2535 False Here we have a Japanese Irish Setter. Lost eye... None ... False NaN NaN NaN 532 False NaN <a href="http://twitter.com/download/iphone" r... False {'id': 4196983835, 'id_str': '4196983835', 'na...

2354 rows × 31 columns

In [13]:
## Selecting only the columns required for analysis
tweets_api=tweets_api.loc[:,['id','favorite_count','retweet_count']]
In [14]:
## Displaying the dataframe
tweets_api.head()
Out[14]:
id favorite_count retweet_count
0 892420643555336193 39467 8853
1 892177421306343426 33819 6514
2 891815181378084864 25461 4328
3 891689557279858688 42908 8964
4 891327558926688256 41048 9774

Assessing

In [15]:
df1
Out[15]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name doggo floofer pupper puppo
0 892420643555336193 NaN NaN 2017-08-01 16:23:56 +0000 <a href="http://twitter.com/download/iphone" r... This is Phineas. He's a mystical boy. Only eve... NaN NaN NaN https://twitter.com/dog_rates/status/892420643... 13 10 Phineas None None None None
1 892177421306343426 NaN NaN 2017-08-01 00:17:27 +0000 <a href="http://twitter.com/download/iphone" r... This is Tilly. She's just checking pup on you.... NaN NaN NaN https://twitter.com/dog_rates/status/892177421... 13 10 Tilly None None None None
2 891815181378084864 NaN NaN 2017-07-31 00:18:03 +0000 <a href="http://twitter.com/download/iphone" r... This is Archie. He is a rare Norwegian Pouncin... NaN NaN NaN https://twitter.com/dog_rates/status/891815181... 12 10 Archie None None None None
3 891689557279858688 NaN NaN 2017-07-30 15:58:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Darla. She commenced a snooze mid meal... NaN NaN NaN https://twitter.com/dog_rates/status/891689557... 13 10 Darla None None None None
4 891327558926688256 NaN NaN 2017-07-29 16:00:24 +0000 <a href="http://twitter.com/download/iphone" r... This is Franklin. He would like you to stop ca... NaN NaN NaN https://twitter.com/dog_rates/status/891327558... 12 10 Franklin None None None None
5 891087950875897856 NaN NaN 2017-07-29 00:08:17 +0000 <a href="http://twitter.com/download/iphone" r... Here we have a majestic great white breaching ... NaN NaN NaN https://twitter.com/dog_rates/status/891087950... 13 10 None None None None None
6 890971913173991426 NaN NaN 2017-07-28 16:27:12 +0000 <a href="http://twitter.com/download/iphone" r... Meet Jax. He enjoys ice cream so much he gets ... NaN NaN NaN https://gofundme.com/ydvmve-surgery-for-jax,ht... 13 10 Jax None None None None
7 890729181411237888 NaN NaN 2017-07-28 00:22:40 +0000 <a href="http://twitter.com/download/iphone" r... When you watch your owner call another dog a g... NaN NaN NaN https://twitter.com/dog_rates/status/890729181... 13 10 None None None None None
8 890609185150312448 NaN NaN 2017-07-27 16:25:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Zoey. She doesn't want to be one of th... NaN NaN NaN https://twitter.com/dog_rates/status/890609185... 13 10 Zoey None None None None
9 890240255349198849 NaN NaN 2017-07-26 15:59:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Cassie. She is a college pup. Studying... NaN NaN NaN https://twitter.com/dog_rates/status/890240255... 14 10 Cassie doggo None None None
10 890006608113172480 NaN NaN 2017-07-26 00:31:25 +0000 <a href="http://twitter.com/download/iphone" r... This is Koda. He is a South Australian decksha... NaN NaN NaN https://twitter.com/dog_rates/status/890006608... 13 10 Koda None None None None
11 889880896479866881 NaN NaN 2017-07-25 16:11:53 +0000 <a href="http://twitter.com/download/iphone" r... This is Bruno. He is a service shark. Only get... NaN NaN NaN https://twitter.com/dog_rates/status/889880896... 13 10 Bruno None None None None
12 889665388333682689 NaN NaN 2017-07-25 01:55:32 +0000 <a href="http://twitter.com/download/iphone" r... Here's a puppo that seems to be on the fence a... NaN NaN NaN https://twitter.com/dog_rates/status/889665388... 13 10 None None None None puppo
13 889638837579907072 NaN NaN 2017-07-25 00:10:02 +0000 <a href="http://twitter.com/download/iphone" r... This is Ted. He does his best. Sometimes that'... NaN NaN NaN https://twitter.com/dog_rates/status/889638837... 12 10 Ted None None None None
14 889531135344209921 NaN NaN 2017-07-24 17:02:04 +0000 <a href="http://twitter.com/download/iphone" r... This is Stuart. He's sporting his favorite fan... NaN NaN NaN https://twitter.com/dog_rates/status/889531135... 13 10 Stuart None None None puppo
15 889278841981685760 NaN NaN 2017-07-24 00:19:32 +0000 <a href="http://twitter.com/download/iphone" r... This is Oliver. You're witnessing one of his m... NaN NaN NaN https://twitter.com/dog_rates/status/889278841... 13 10 Oliver None None None None
16 888917238123831296 NaN NaN 2017-07-23 00:22:39 +0000 <a href="http://twitter.com/download/iphone" r... This is Jim. He found a fren. Taught him how t... NaN NaN NaN https://twitter.com/dog_rates/status/888917238... 12 10 Jim None None None None
17 888804989199671297 NaN NaN 2017-07-22 16:56:37 +0000 <a href="http://twitter.com/download/iphone" r... This is Zeke. He has a new stick. Very proud o... NaN NaN NaN https://twitter.com/dog_rates/status/888804989... 13 10 Zeke None None None None
18 888554962724278272 NaN NaN 2017-07-22 00:23:06 +0000 <a href="http://twitter.com/download/iphone" r... This is Ralphus. He's powering up. Attempting ... NaN NaN NaN https://twitter.com/dog_rates/status/888554962... 13 10 Ralphus None None None None
19 888202515573088257 NaN NaN 2017-07-21 01:02:36 +0000 <a href="http://twitter.com/download/iphone" r... RT @dog_rates: This is Canela. She attempted s... 8.874740e+17 4.196984e+09 2017-07-19 00:47:34 +0000 https://twitter.com/dog_rates/status/887473957... 13 10 Canela None None None None
20 888078434458587136 NaN NaN 2017-07-20 16:49:33 +0000 <a href="http://twitter.com/download/iphone" r... This is Gerald. He was just told he didn't get... NaN NaN NaN https://twitter.com/dog_rates/status/888078434... 12 10 Gerald None None None None
21 887705289381826560 NaN NaN 2017-07-19 16:06:48 +0000 <a href="http://twitter.com/download/iphone" r... This is Jeffrey. He has a monopoly on the pool... NaN NaN NaN https://twitter.com/dog_rates/status/887705289... 13 10 Jeffrey None None None None
22 887517139158093824 NaN NaN 2017-07-19 03:39:09 +0000 <a href="http://twitter.com/download/iphone" r... I've yet to rate a Venezuelan Hover Wiener. Th... NaN NaN NaN https://twitter.com/dog_rates/status/887517139... 14 10 such None None None None
23 887473957103951883 NaN NaN 2017-07-19 00:47:34 +0000 <a href="http://twitter.com/download/iphone" r... This is Canela. She attempted some fancy porch... NaN NaN NaN https://twitter.com/dog_rates/status/887473957... 13 10 Canela None None None None
24 887343217045368832 NaN NaN 2017-07-18 16:08:03 +0000 <a href="http://twitter.com/download/iphone" r... You may not have known you needed to see this ... NaN NaN NaN https://twitter.com/dog_rates/status/887343217... 13 10 None None None None None
25 887101392804085760 NaN NaN 2017-07-18 00:07:08 +0000 <a href="http://twitter.com/download/iphone" r... This... is a Jubilant Antarctic House Bear. We... NaN NaN NaN https://twitter.com/dog_rates/status/887101392... 12 10 None None None None None
26 886983233522544640 NaN NaN 2017-07-17 16:17:36 +0000 <a href="http://twitter.com/download/iphone" r... This is Maya. She's very shy. Rarely leaves he... NaN NaN NaN https://twitter.com/dog_rates/status/886983233... 13 10 Maya None None None None
27 886736880519319552 NaN NaN 2017-07-16 23:58:41 +0000 <a href="http://twitter.com/download/iphone" r... This is Mingus. He's a wonderful father to his... NaN NaN NaN https://www.gofundme.com/mingusneedsus,https:/... 13 10 Mingus None None None None
28 886680336477933568 NaN NaN 2017-07-16 20:14:00 +0000 <a href="http://twitter.com/download/iphone" r... This is Derek. He's late for a dog meeting. 13... NaN NaN NaN https://twitter.com/dog_rates/status/886680336... 13 10 Derek None None None None
29 886366144734445568 NaN NaN 2017-07-15 23:25:31 +0000 <a href="http://twitter.com/download/iphone" r... This is Roscoe. Another pupper fallen victim t... NaN NaN NaN https://twitter.com/dog_rates/status/886366144... 12 10 Roscoe None None pupper None
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2326 666411507551481857 NaN NaN 2015-11-17 00:24:19 +0000 <a href="http://twitter.com/download/iphone" r... This is quite the dog. Gets really excited whe... NaN NaN NaN https://twitter.com/dog_rates/status/666411507... 2 10 quite None None None None
2327 666407126856765440 NaN NaN 2015-11-17 00:06:54 +0000 <a href="http://twitter.com/download/iphone" r... This is a southern Vesuvius bumblegruff. Can d... NaN NaN NaN https://twitter.com/dog_rates/status/666407126... 7 10 a None None None None
2328 666396247373291520 NaN NaN 2015-11-16 23:23:41 +0000 <a href="http://twitter.com/download/iphone" r... Oh goodness. A super rare northeast Qdoba kang... NaN NaN NaN https://twitter.com/dog_rates/status/666396247... 9 10 None None None None None
2329 666373753744588802 NaN NaN 2015-11-16 21:54:18 +0000 <a href="http://twitter.com/download/iphone" r... Those are sunglasses and a jean jacket. 11/10 ... NaN NaN NaN https://twitter.com/dog_rates/status/666373753... 11 10 None None None None None
2330 666362758909284353 NaN NaN 2015-11-16 21:10:36 +0000 <a href="http://twitter.com/download/iphone" r... Unique dog here. Very small. Lives in containe... NaN NaN NaN https://twitter.com/dog_rates/status/666362758... 6 10 None None None None None
2331 666353288456101888 NaN NaN 2015-11-16 20:32:58 +0000 <a href="http://twitter.com/download/iphone" r... Here we have a mixed Asiago from the Galápagos... NaN NaN NaN https://twitter.com/dog_rates/status/666353288... 8 10 None None None None None
2332 666345417576210432 NaN NaN 2015-11-16 20:01:42 +0000 <a href="http://twitter.com/download/iphone" r... Look at this jokester thinking seat belt laws ... NaN NaN NaN https://twitter.com/dog_rates/status/666345417... 10 10 None None None None None
2333 666337882303524864 NaN NaN 2015-11-16 19:31:45 +0000 <a href="http://twitter.com/download/iphone" r... This is an extremely rare horned Parthenon. No... NaN NaN NaN https://twitter.com/dog_rates/status/666337882... 9 10 an None None None None
2334 666293911632134144 NaN NaN 2015-11-16 16:37:02 +0000 <a href="http://twitter.com/download/iphone" r... This is a funny dog. Weird toes. Won't come do... NaN NaN NaN https://twitter.com/dog_rates/status/666293911... 3 10 a None None None None
2335 666287406224695296 NaN NaN 2015-11-16 16:11:11 +0000 <a href="http://twitter.com/download/iphone" r... This is an Albanian 3 1/2 legged Episcopalian... NaN NaN NaN https://twitter.com/dog_rates/status/666287406... 1 2 an None None None None
2336 666273097616637952 NaN NaN 2015-11-16 15:14:19 +0000 <a href="http://twitter.com/download/iphone" r... Can take selfies 11/10 https://t.co/ws2AMaNwPW NaN NaN NaN https://twitter.com/dog_rates/status/666273097... 11 10 None None None None None
2337 666268910803644416 NaN NaN 2015-11-16 14:57:41 +0000 <a href="http://twitter.com/download/iphone" r... Very concerned about fellow dog trapped in com... NaN NaN NaN https://twitter.com/dog_rates/status/666268910... 10 10 None None None None None
2338 666104133288665088 NaN NaN 2015-11-16 04:02:55 +0000 <a href="http://twitter.com/download/iphone" r... Not familiar with this breed. No tail (weird).... NaN NaN NaN https://twitter.com/dog_rates/status/666104133... 1 10 None None None None None
2339 666102155909144576 NaN NaN 2015-11-16 03:55:04 +0000 <a href="http://twitter.com/download/iphone" r... Oh my. Here you are seeing an Adobe Setter giv... NaN NaN NaN https://twitter.com/dog_rates/status/666102155... 11 10 None None None None None
2340 666099513787052032 NaN NaN 2015-11-16 03:44:34 +0000 <a href="http://twitter.com/download/iphone" r... Can stand on stump for what seems like a while... NaN NaN NaN https://twitter.com/dog_rates/status/666099513... 8 10 None None None None None
2341 666094000022159362 NaN NaN 2015-11-16 03:22:39 +0000 <a href="http://twitter.com/download/iphone" r... This appears to be a Mongolian Presbyterian mi... NaN NaN NaN https://twitter.com/dog_rates/status/666094000... 9 10 None None None None None
2342 666082916733198337 NaN NaN 2015-11-16 02:38:37 +0000 <a href="http://twitter.com/download/iphone" r... Here we have a well-established sunblockerspan... NaN NaN NaN https://twitter.com/dog_rates/status/666082916... 6 10 None None None None None
2343 666073100786774016 NaN NaN 2015-11-16 01:59:36 +0000 <a href="http://twitter.com/download/iphone" r... Let's hope this flight isn't Malaysian (lol). ... NaN NaN NaN https://twitter.com/dog_rates/status/666073100... 10 10 None None None None None
2344 666071193221509120 NaN NaN 2015-11-16 01:52:02 +0000 <a href="http://twitter.com/download/iphone" r... Here we have a northern speckled Rhododendron.... NaN NaN NaN https://twitter.com/dog_rates/status/666071193... 9 10 None None None None None
2345 666063827256086533 NaN NaN 2015-11-16 01:22:45 +0000 <a href="http://twitter.com/download/iphone" r... This is the happiest dog you will ever see. Ve... NaN NaN NaN https://twitter.com/dog_rates/status/666063827... 10 10 the None None None None
2346 666058600524156928 NaN NaN 2015-11-16 01:01:59 +0000 <a href="http://twitter.com/download/iphone" r... Here is the Rand Paul of retrievers folks! He'... NaN NaN NaN https://twitter.com/dog_rates/status/666058600... 8 10 the None None None None
2347 666057090499244032 NaN NaN 2015-11-16 00:55:59 +0000 <a href="http://twitter.com/download/iphone" r... My oh my. This is a rare blond Canadian terrie... NaN NaN NaN https://twitter.com/dog_rates/status/666057090... 9 10 a None None None None
2348 666055525042405380 NaN NaN 2015-11-16 00:49:46 +0000 <a href="http://twitter.com/download/iphone" r... Here is a Siberian heavily armored polar bear ... NaN NaN NaN https://twitter.com/dog_rates/status/666055525... 10 10 a None None None None
2349 666051853826850816 NaN NaN 2015-11-16 00:35:11 +0000 <a href="http://twitter.com/download/iphone" r... This is an odd dog. Hard on the outside but lo... NaN NaN NaN https://twitter.com/dog_rates/status/666051853... 2 10 an None None None None
2350 666050758794694657 NaN NaN 2015-11-16 00:30:50 +0000 <a href="http://twitter.com/download/iphone" r... This is a truly beautiful English Wilson Staff... NaN NaN NaN https://twitter.com/dog_rates/status/666050758... 10 10 a None None None None
2351 666049248165822465 NaN NaN 2015-11-16 00:24:50 +0000 <a href="http://twitter.com/download/iphone" r... Here we have a 1949 1st generation vulpix. Enj... NaN NaN NaN https://twitter.com/dog_rates/status/666049248... 5 10 None None None None None
2352 666044226329800704 NaN NaN 2015-11-16 00:04:52 +0000 <a href="http://twitter.com/download/iphone" r... This is a purebred Piers Morgan. Loves to Netf... NaN NaN NaN https://twitter.com/dog_rates/status/666044226... 6 10 a None None None None
2353 666033412701032449 NaN NaN 2015-11-15 23:21:54 +0000 <a href="http://twitter.com/download/iphone" r... Here is a very happy pup. Big fan of well-main... NaN NaN NaN https://twitter.com/dog_rates/status/666033412... 9 10 a None None None None
2354 666029285002620928 NaN NaN 2015-11-15 23:05:30 +0000 <a href="http://twitter.com/download/iphone" r... This is a western brown Mitsubishi terrier. Up... NaN NaN NaN https://twitter.com/dog_rates/status/666029285... 7 10 a None None None None
2355 666020888022790149 NaN NaN 2015-11-15 22:32:08 +0000 <a href="http://twitter.com/download/iphone" r... Here we have a Japanese Irish Setter. Lost eye... NaN NaN NaN https://twitter.com/dog_rates/status/666020888... 8 10 None None None None None

2356 rows × 17 columns

In [16]:
##df1 we can see the completeness issues
df1.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
tweet_id                      2356 non-null int64
in_reply_to_status_id         78 non-null float64
in_reply_to_user_id           78 non-null float64
timestamp                     2356 non-null object
source                        2356 non-null object
text                          2356 non-null object
retweeted_status_id           181 non-null float64
retweeted_status_user_id      181 non-null float64
retweeted_status_timestamp    181 non-null object
expanded_urls                 2297 non-null object
rating_numerator              2356 non-null int64
rating_denominator            2356 non-null int64
name                          2356 non-null object
doggo                         2356 non-null object
floofer                       2356 non-null object
pupper                        2356 non-null object
puppo                         2356 non-null object
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

We can see that there are lot of null items tin in_reply_to_status_id,in_reply_to_user_id, retweeted_status_user_id,retweeted_status_user_id,retweeted_status_timestamp and there are some missing values in expanded_urls.Sometimes we can correct the compelteness issues but getting together with the stakeholders and soemtimes we have to leave it behind because we don't have any data source to extract data. But in this case we won't be using these columns so we will be removing these columns from the table

In [17]:
sum(df1['retweeted_status_id'].notnull())
Out[17]:
181

Remove retweeted_status_id (181 columns) as we are only interested in original tweet

In [18]:
type(df1['timestamp'][0])
Out[18]:
str
In [19]:
type(df1['tweet_id'][0])
Out[19]:
numpy.int64

Time in df1 dataframe is string we need to convert time into timestamp for anlysis, tweet_id should be a string

In [20]:
df1['tweet_id'].value_counts()
Out[20]:
749075273010798592    1
741099773336379392    1
798644042770751489    1
825120256414846976    1
769212283578875904    1
700462010979500032    1
780858289093574656    1
699775878809702401    1
880095782870896641    1
760521673607086080    1
776477788987613185    1
691820333922455552    1
715696743237730304    1
714606013974974464    1
760539183865880579    1
813157409116065792    1
676430933382295552    1
743510151680958465    1
837012587749474308    1
833722901757046785    1
818259473185828864    1
670704688707301377    1
667160273090932737    1
674394782723014656    1
672082170312290304    1
670093938074779648    1
759923798737051648    1
809920764300447744    1
805487436403003392    1
838085839343206401    1
                     ..
763956972077010945    1
870308999962521604    1
720775346191278080    1
785927819176054784    1
783347506784731136    1
775733305207554048    1
834209720923721728    1
825026590719483904    1
758405701903519748    1
668986018524233728    1
690938899477221376    1
667911425562669056    1
754482103782404096    1
713175907180089344    1
669015743032369152    1
672068090318987265    1
816829038950027264    1
683773439333797890    1
674291837063053312    1
837482249356513284    1
767500508068192258    1
773922284943896577    1
673342308415348736    1
886054160059072513    1
748307329658011649    1
715360349751484417    1
666817836334096384    1
794926597468000259    1
673705679337693185    1
700151421916807169    1
Name: tweet_id, Length: 2356, dtype: int64
In [21]:
df1['name']
Out[21]:
0        Phineas
1          Tilly
2         Archie
3          Darla
4       Franklin
5           None
6            Jax
7           None
8           Zoey
9         Cassie
10          Koda
11         Bruno
12          None
13           Ted
14        Stuart
15        Oliver
16           Jim
17          Zeke
18       Ralphus
19        Canela
20        Gerald
21       Jeffrey
22          such
23        Canela
24          None
25          None
26          Maya
27        Mingus
28         Derek
29        Roscoe
          ...   
2326       quite
2327           a
2328        None
2329        None
2330        None
2331        None
2332        None
2333          an
2334           a
2335          an
2336        None
2337        None
2338        None
2339        None
2340        None
2341        None
2342        None
2343        None
2344        None
2345         the
2346         the
2347           a
2348           a
2349          an
2350           a
2351        None
2352           a
2353           a
2354           a
2355        None
Name: name, Length: 2356, dtype: object
In [22]:
df1['name'].value_counts()
Out[22]:
None         745
a             55
Charlie       12
Lucy          11
Cooper        11
Oliver        11
Penny         10
Tucker        10
Lola          10
Winston        9
Bo             9
the            8
Sadie          8
an             7
Toby           7
Buddy          7
Daisy          7
Bailey         7
Rusty          6
Jack           6
Bella          6
Koda           6
Leo            6
Oscar          6
Jax            6
Stanley        6
Scout          6
Milo           6
Dave           6
George         5
            ... 
Ralphy         1
Oreo           1
Eazy           1
Rizzo          1
Keet           1
Carll          1
Crawford       1
Kona           1
Blue           1
Ulysses        1
Reptar         1
Halo           1
Barney         1
Dunkin         1
Filup          1
Ralpher        1
Puff           1
Karl           1
Gert           1
Edd            1
Milky          1
Enchilada      1
Harvey         1
Tyrus          1
Harlso         1
Remy           1
Cilantro       1
Darby          1
Mairi          1
Batdog         1
Name: name, Length: 957, dtype: int64
In [23]:
## Checking if there are any names in lower case
df1.name[df1.name.str.islower()]
Out[23]:
22            such
56               a
118          quite
169          quite
193          quite
335            not
369            one
542     incredibly
649              a
682            mad
759             an
773           very
801              a
819           very
822           just
852             my
924            one
988            not
992            his
993            one
1002             a
1004             a
1017             a
1025            an
1031          very
1040      actually
1049             a
1063          just
1071       getting
1095           mad
           ...    
2191             a
2198             a
2204            an
2211             a
2212           the
2218             a
2222             a
2235             a
2249             a
2255             a
2264             a
2273             a
2287             a
2304             a
2311             a
2314             a
2326         quite
2327             a
2333            an
2334             a
2335            an
2345           the
2346           the
2347             a
2348             a
2349            an
2350             a
2352             a
2353             a
2354             a
Name: name, Length: 109, dtype: object

his, a,the,a,an,quite,such and none should not be in name

In [24]:
df1['source'].value_counts()
Out[24]:
<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>     2221
<a href="http://vine.co" rel="nofollow">Vine - Make a Scene</a>                          91
<a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>                       33
<a href="https://about.twitter.com/products/tweetdeck" rel="nofollow">TweetDeck</a>      11
Name: source, dtype: int64
In [25]:
## The information about the source is in the url// and is not easily readible
In [26]:
## There are lot of none values in the dataframe
In [27]:
df1['expanded_urls'].head(20)
Out[27]:
0     https://twitter.com/dog_rates/status/892420643...
1     https://twitter.com/dog_rates/status/892177421...
2     https://twitter.com/dog_rates/status/891815181...
3     https://twitter.com/dog_rates/status/891689557...
4     https://twitter.com/dog_rates/status/891327558...
5     https://twitter.com/dog_rates/status/891087950...
6     https://gofundme.com/ydvmve-surgery-for-jax,ht...
7     https://twitter.com/dog_rates/status/890729181...
8     https://twitter.com/dog_rates/status/890609185...
9     https://twitter.com/dog_rates/status/890240255...
10    https://twitter.com/dog_rates/status/890006608...
11    https://twitter.com/dog_rates/status/889880896...
12    https://twitter.com/dog_rates/status/889665388...
13    https://twitter.com/dog_rates/status/889638837...
14    https://twitter.com/dog_rates/status/889531135...
15    https://twitter.com/dog_rates/status/889278841...
16    https://twitter.com/dog_rates/status/888917238...
17    https://twitter.com/dog_rates/status/888804989...
18    https://twitter.com/dog_rates/status/888554962...
19    https://twitter.com/dog_rates/status/887473957...
Name: expanded_urls, dtype: object
In [28]:
df1.expanded_urls[df1.expanded_urls.isna()].shape
Out[28]:
(59,)
In [29]:
## Missing values in exapnded url(59)
In [30]:
df1['rating_numerator'].value_counts()
Out[30]:
12      558
11      464
10      461
13      351
9       158
8       102
7        55
14       54
5        37
6        32
3        19
4        17
1         9
2         9
420       2
0         2
15        2
75        2
80        1
20        1
24        1
26        1
44        1
50        1
60        1
165       1
84        1
88        1
144       1
182       1
143       1
666       1
960       1
1776      1
17        1
27        1
45        1
99        1
121       1
204       1
Name: rating_numerator, dtype: int64
In [31]:
df1['rating_denominator'].value_counts()
Out[31]:
10     2333
11        3
50        3
80        2
20        2
2         1
16        1
40        1
70        1
15        1
90        1
110       1
120       1
130       1
150       1
170       1
7         1
0         1
Name: rating_denominator, dtype: int64
In [32]:
df1.query('rating_numerator == 1776')
Out[32]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name doggo floofer pupper puppo
979 749981277374128128 NaN NaN 2016-07-04 15:00:45 +0000 <a href="https://about.twitter.com/products/tw... This is Atticus. He's quite simply America af.... NaN NaN NaN https://twitter.com/dog_rates/status/749981277... 1776 10 Atticus None None None None
In [33]:
## Incorrect data for the denominator --> the denominator have values other than 10
In [34]:
## Visual Assessment
imagedf1.head(20)
Out[34]:
tweet_id jpg_url img_num p1 p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog
0 666020888022790149 https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg 1 Welsh_springer_spaniel 0.465074 True collie 0.156665 True Shetland_sheepdog 0.061428 True
1 666029285002620928 https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg 1 redbone 0.506826 True miniature_pinscher 0.074192 True Rhodesian_ridgeback 0.072010 True
2 666033412701032449 https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg 1 German_shepherd 0.596461 True malinois 0.138584 True bloodhound 0.116197 True
3 666044226329800704 https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg 1 Rhodesian_ridgeback 0.408143 True redbone 0.360687 True miniature_pinscher 0.222752 True
4 666049248165822465 https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg 1 miniature_pinscher 0.560311 True Rottweiler 0.243682 True Doberman 0.154629 True
5 666050758794694657 https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg 1 Bernese_mountain_dog 0.651137 True English_springer 0.263788 True Greater_Swiss_Mountain_dog 0.016199 True
6 666051853826850816 https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg 1 box_turtle 0.933012 False mud_turtle 0.045885 False terrapin 0.017885 False
7 666055525042405380 https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg 1 chow 0.692517 True Tibetan_mastiff 0.058279 True fur_coat 0.054449 False
8 666057090499244032 https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg 1 shopping_cart 0.962465 False shopping_basket 0.014594 False golden_retriever 0.007959 True
9 666058600524156928 https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg 1 miniature_poodle 0.201493 True komondor 0.192305 True soft-coated_wheaten_terrier 0.082086 True
10 666063827256086533 https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg 1 golden_retriever 0.775930 True Tibetan_mastiff 0.093718 True Labrador_retriever 0.072427 True
11 666071193221509120 https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg 1 Gordon_setter 0.503672 True Yorkshire_terrier 0.174201 True Pekinese 0.109454 True
12 666073100786774016 https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg 1 Walker_hound 0.260857 True English_foxhound 0.175382 True Ibizan_hound 0.097471 True
13 666082916733198337 https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg 1 pug 0.489814 True bull_mastiff 0.404722 True French_bulldog 0.048960 True
14 666094000022159362 https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg 1 bloodhound 0.195217 True German_shepherd 0.078260 True malinois 0.075628 True
15 666099513787052032 https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg 1 Lhasa 0.582330 True Shih-Tzu 0.166192 True Dandie_Dinmont 0.089688 True
16 666102155909144576 https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg 1 English_setter 0.298617 True Newfoundland 0.149842 True borzoi 0.133649 True
17 666104133288665088 https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg 1 hen 0.965932 False cock 0.033919 False partridge 0.000052 False
18 666268910803644416 https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg 1 desktop_computer 0.086502 False desk 0.085547 False bookcase 0.079480 False
19 666273097616637952 https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg 1 Italian_greyhound 0.176053 True toy_terrier 0.111884 True basenji 0.111152 True
In [35]:
imagedf1.tail(20)
Out[35]:
tweet_id jpg_url img_num p1 p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog
2055 888202515573088257 https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg 2 Pembroke 0.809197 True Rhodesian_ridgeback 0.054950 True beagle 0.038915 True
2056 888554962724278272 https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg 3 Siberian_husky 0.700377 True Eskimo_dog 0.166511 True malamute 0.111411 True
2057 888804989199671297 https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg 1 golden_retriever 0.469760 True Labrador_retriever 0.184172 True English_setter 0.073482 True
2058 888917238123831296 https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg 1 golden_retriever 0.714719 True Tibetan_mastiff 0.120184 True Labrador_retriever 0.105506 True
2059 889278841981685760 https://pbs.twimg.com/ext_tw_video_thumb/88927... 1 whippet 0.626152 True borzoi 0.194742 True Saluki 0.027351 True
2060 889531135344209921 https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg 1 golden_retriever 0.953442 True Labrador_retriever 0.013834 True redbone 0.007958 True
2061 889638837579907072 https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg 1 French_bulldog 0.991650 True boxer 0.002129 True Staffordshire_bullterrier 0.001498 True
2062 889665388333682689 https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg 1 Pembroke 0.966327 True Cardigan 0.027356 True basenji 0.004633 True
2063 889880896479866881 https://pbs.twimg.com/media/DFl99B1WsAITKsg.jpg 1 French_bulldog 0.377417 True Labrador_retriever 0.151317 True muzzle 0.082981 False
2064 890006608113172480 https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg 1 Samoyed 0.957979 True Pomeranian 0.013884 True chow 0.008167 True
2065 890240255349198849 https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg 1 Pembroke 0.511319 True Cardigan 0.451038 True Chihuahua 0.029248 True
2066 890609185150312448 https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg 1 Irish_terrier 0.487574 True Irish_setter 0.193054 True Chesapeake_Bay_retriever 0.118184 True
2067 890729181411237888 https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg 2 Pomeranian 0.566142 True Eskimo_dog 0.178406 True Pembroke 0.076507 True
2068 890971913173991426 https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg 1 Appenzeller 0.341703 True Border_collie 0.199287 True ice_lolly 0.193548 False
2069 891087950875897856 https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg 1 Chesapeake_Bay_retriever 0.425595 True Irish_terrier 0.116317 True Indian_elephant 0.076902 False
2070 891327558926688256 https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg 2 basset 0.555712 True English_springer 0.225770 True German_short-haired_pointer 0.175219 True
2071 891689557279858688 https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg 1 paper_towel 0.170278 False Labrador_retriever 0.168086 True spatula 0.040836 False
2072 891815181378084864 https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg 1 Chihuahua 0.716012 True malamute 0.078253 True kelpie 0.031379 True
2073 892177421306343426 https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg 1 Chihuahua 0.323581 True Pekinese 0.090647 True papillon 0.068957 True
2074 892420643555336193 https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg 1 orange 0.097049 False bagel 0.085851 False banana 0.076110 False
In [36]:
imagedf1.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
tweet_id    2075 non-null int64
jpg_url     2075 non-null object
img_num     2075 non-null int64
p1          2075 non-null object
p1_conf     2075 non-null float64
p1_dog      2075 non-null bool
p2          2075 non-null object
p2_conf     2075 non-null float64
p2_dog      2075 non-null bool
p3          2075 non-null object
p3_conf     2075 non-null float64
p3_dog      2075 non-null bool
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB
In [37]:
## there are no null values but the total count of twitter data is 2356(suspect that 281 records might be retweeted)
In [38]:
## The column not informative (p1,p1_conf,p1_dog,p2,p2_conf,p2_dog,p3,p3_conf,p3_dog)
In [39]:
imagedf1['p1'].value_counts()
Out[39]:
golden_retriever             150
Labrador_retriever           100
Pembroke                      89
Chihuahua                     83
pug                           57
chow                          44
Samoyed                       43
toy_poodle                    39
Pomeranian                    38
cocker_spaniel                30
malamute                      30
French_bulldog                26
Chesapeake_Bay_retriever      23
miniature_pinscher            23
seat_belt                     22
German_shepherd               20
Siberian_husky                20
Staffordshire_bullterrier     20
Cardigan                      19
web_site                      19
Shetland_sheepdog             18
Maltese_dog                   18
Eskimo_dog                    18
teddy                         18
beagle                        18
Rottweiler                    17
Lakeland_terrier              17
Shih-Tzu                      17
kuvasz                        16
Italian_greyhound             16
                            ... 
traffic_light                  1
envelope                       1
soccer_ball                    1
African_crocodile              1
cheetah                        1
rapeseed                       1
guenon                         1
flamingo                       1
four-poster                    1
bighorn                        1
mailbox                        1
lorikeet                       1
remote_control                 1
radio_telescope                1
rotisserie                     1
boathouse                      1
black-footed_ferret            1
washer                         1
pool_table                     1
snowmobile                     1
padlock                        1
terrapin                       1
lion                           1
china_cabinet                  1
scorpion                       1
Egyptian_cat                   1
maillot                        1
basketball                     1
sea_urchin                     1
zebra                          1
Name: p1, Length: 378, dtype: int64
In [40]:
## the dog names in the p1_column are in upper and lower case
In [41]:
imagedf1['p2'].value_counts()
Out[41]:
Labrador_retriever                104
golden_retriever                   92
Cardigan                           73
Chihuahua                          44
Pomeranian                         42
Chesapeake_Bay_retriever           41
French_bulldog                     41
toy_poodle                         37
cocker_spaniel                     34
Siberian_husky                     33
miniature_poodle                   33
beagle                             28
collie                             27
Pembroke                           27
Eskimo_dog                         27
kuvasz                             26
Italian_greyhound                  22
Pekinese                           21
American_Staffordshire_terrier     21
miniature_pinscher                 20
malinois                           20
toy_terrier                        20
Samoyed                            20
chow                               20
Norwegian_elkhound                 19
Boston_bull                        19
Staffordshire_bullterrier          18
Irish_terrier                      17
pug                                17
Shih-Tzu                           16
                                 ... 
stingray                            1
saltshaker                          1
standard_schnauzer                  1
hotdog                              1
space_heater                        1
racket                              1
grey_fox                            1
spatula                             1
sea_urchin                          1
hay                                 1
dugong                              1
ice_lolly                           1
pier                                1
necklace                            1
wood_rabbit                         1
breastplate                         1
boathouse                           1
neck_brace                          1
washbasin                           1
streetcar                           1
volcano                             1
leafhopper                          1
oxygen_mask                         1
mailbox                             1
timber_wolf                         1
tick                                1
rifle                               1
comic_book                          1
hand-held_computer                  1
African_hunting_dog                 1
Name: p2, Length: 405, dtype: int64
In [42]:
## the dog names in column p2 are in upper and lower case
In [43]:
imagedf1['p3'].value_counts()
Out[43]:
Labrador_retriever                79
Chihuahua                         58
golden_retriever                  48
Eskimo_dog                        38
kelpie                            35
kuvasz                            34
chow                              32
Staffordshire_bullterrier         32
cocker_spaniel                    31
beagle                            31
Pomeranian                        29
Pekinese                          29
toy_poodle                        29
Great_Pyrenees                    27
Pembroke                          27
Chesapeake_Bay_retriever          27
malamute                          26
French_bulldog                    26
American_Staffordshire_terrier    24
Cardigan                          23
pug                               23
basenji                           21
bull_mastiff                      20
toy_terrier                       20
Siberian_husky                    19
Boston_bull                       17
Shetland_sheepdog                 17
doormat                           16
Lakeland_terrier                  16
boxer                             16
                                  ..
switch                             1
nipple                             1
space_shuttle                      1
Sussex_spaniel                     1
pretzel                            1
go-kart                            1
parachute                          1
pier                               1
viaduct                            1
valley                             1
dugong                             1
mitten                             1
padlock                            1
canoe                              1
hand-held_computer                 1
wok                                1
meerkat                            1
drumstick                          1
rifle                              1
theater_curtain                    1
moped                              1
spatula                            1
vacuum                             1
neck_brace                         1
shower_cap                         1
bullfrog                           1
electric_fan                       1
European_fire_salamander           1
soap_dispenser                     1
zebra                              1
Name: p3, Length: 408, dtype: int64
In [44]:
## the dog names in column p3 are in upper and lower case
In [45]:
## Checking for duplicate tweet_id column
imagedf1.tweet_id.value_counts()
Out[45]:
685532292383666176    1
826598365270007810    1
692158366030913536    1
714606013974974464    1
715696743237730304    1
776477788987613185    1
772114945936949249    1
699775878809702401    1
780858289093574656    1
700462010979500032    1
732726085725589504    1
738883359779196928    1
798644042770751489    1
743510151680958465    1
837012587749474308    1
833722901757046785    1
668620235289837568    1
842765311967449089    1
685315239903100929    1
673686845050527744    1
680473011644985345    1
666051853826850816    1
675853064436391936    1
693231807727280129    1
705475953783398401    1
829449946868879360    1
759923798737051648    1
667160273090932737    1
680934982542561280    1
743545585370791937    1
                     ..
794926597468000259    1
776113305656188928    1
825026590719483904    1
834209720923721728    1
775733305207554048    1
669564461267722241    1
879492040517615616    1
720775346191278080    1
666362758909284353    1
750506206503038976    1
693155686491000832    1
793601777308463104    1
740373189193256964    1
754482103782404096    1
881536004380872706    1
843604394117681152    1
748307329658011649    1
759846353224826880    1
885984800019947520    1
773922284943896577    1
666345417576210432    1
837482249356513284    1
812781120811126785    1
870804317367881728    1
790698755171364864    1
816829038950027264    1
847971574464610304    1
713175907180089344    1
670338931251150849    1
700151421916807169    1
Name: tweet_id, Length: 2075, dtype: int64
In [46]:
## there are no duplicated tweet_id's
In [47]:
##Checking for duplicated url
imagedf1[imagedf1['jpg_url'].duplicated()]
Out[47]:
tweet_id jpg_url img_num p1 p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog
1297 752309394570878976 https://pbs.twimg.com/ext_tw_video_thumb/67535... 1 upright 0.303415 False golden_retriever 0.181351 True Brittany_spaniel 0.162084 True
1315 754874841593970688 https://pbs.twimg.com/media/CWza7kpWcAAdYLc.jpg 1 pug 0.272205 True bull_mastiff 0.251530 True bath_towel 0.116806 False
1333 757729163776290825 https://pbs.twimg.com/media/CWyD2HGUYAQ1Xa7.jpg 2 cash_machine 0.802333 False schipperke 0.045519 True German_shepherd 0.023353 True
1345 759159934323924993 https://pbs.twimg.com/media/CU1zsMSUAAAS0qW.jpg 1 Irish_terrier 0.254856 True briard 0.227716 True soft-coated_wheaten_terrier 0.223263 True
1349 759566828574212096 https://pbs.twimg.com/media/CkNjahBXAAQ2kWo.jpg 1 Labrador_retriever 0.967397 True golden_retriever 0.016641 True ice_bear 0.014858 False
1364 761371037149827077 https://pbs.twimg.com/tweet_video_thumb/CeBym7... 1 brown_bear 0.713293 False Indian_elephant 0.172844 False water_buffalo 0.038902 False
1368 761750502866649088 https://pbs.twimg.com/media/CYLDikFWEAAIy1y.jpg 1 golden_retriever 0.586937 True Labrador_retriever 0.398260 True kuvasz 0.005410 True
1387 766078092750233600 https://pbs.twimg.com/media/ChK1tdBWwAQ1flD.jpg 1 toy_poodle 0.420463 True miniature_poodle 0.132640 True Chesapeake_Bay_retriever 0.121523 True
1407 770093767776997377 https://pbs.twimg.com/media/CkjMx99UoAM2B1a.jpg 1 golden_retriever 0.843799 True Labrador_retriever 0.052956 True kelpie 0.035711 True
1417 771171053431250945 https://pbs.twimg.com/media/CVgdFjNWEAAxmbq.jpg 3 Samoyed 0.978833 True Pomeranian 0.012763 True Eskimo_dog 0.001853 True
1427 772615324260794368 https://pbs.twimg.com/media/Cp6db4-XYAAMmqL.jpg 1 dalmatian 0.556595 True whippet 0.151047 True American_Staffordshire_terrier 0.096435 True
1446 775898661951791106 https://pbs.twimg.com/media/CiyHLocU4AI2pJu.jpg 1 golden_retriever 0.945523 True Labrador_retriever 0.042319 True doormat 0.003956 False
1453 776819012571455488 https://pbs.twimg.com/media/CW88XN4WsAAlo8r.jpg 3 Chihuahua 0.346545 True dalmatian 0.166246 True toy_terrier 0.117502 True
1456 777641927919427584 https://pbs.twimg.com/media/CmoPdmHW8AAi8BI.jpg 1 golden_retriever 0.964929 True Labrador_retriever 0.011584 True refrigerator 0.007499 False
1463 778396591732486144 https://pbs.twimg.com/media/CcG07BYW0AErrC9.jpg 1 hippopotamus 0.581403 False doormat 0.152445 False sea_lion 0.026364 False
1476 780496263422808064 https://pbs.twimg.com/media/Ck2d7tJWUAEPTL3.jpg 1 pug 0.997310 True Brabancon_griffon 0.001186 True French_bulldog 0.000428 True
1487 782021823840026624 https://pbs.twimg.com/media/CdHwZd0VIAA4792.jpg 1 golden_retriever 0.383223 True cocker_spaniel 0.165930 True Chesapeake_Bay_retriever 0.118199 True
1495 783347506784731136 https://pbs.twimg.com/media/CVuQ2LeUsAAIe3s.jpg 1 Cardigan 0.611525 True Pembroke 0.368566 True Chihuahua 0.003330 True
1510 786036967502913536 https://pbs.twimg.com/media/CtKHLuCWYAA2TTs.jpg 1 golden_retriever 0.993830 True cocker_spaniel 0.003143 True Great_Pyrenees 0.000917 True
1522 788070120937619456 https://pbs.twimg.com/media/Co-hmcYXYAASkiG.jpg 1 golden_retriever 0.735163 True Sussex_spaniel 0.064897 True Labrador_retriever 0.047704 True
1538 790723298204217344 https://pbs.twimg.com/media/CvaYgDOWgAEfjls.jpg 1 tub 0.479477 False bathtub 0.325106 False golden_retriever 0.078530 True
1541 791026214425268224 https://pbs.twimg.com/media/CpmyNumW8AAAJGj.jpg 1 malamute 0.375098 True jean 0.069362 False keeshond 0.050528 True
1564 793614319594401792 https://pbs.twimg.com/media/CvyVxQRWEAAdSZS.jpg 1 golden_retriever 0.705092 True Labrador_retriever 0.219721 True kuvasz 0.015965 True
1569 794355576146903043 https://pbs.twimg.com/media/CvJCabcWgAIoUxW.jpg 1 cocker_spaniel 0.500509 True golden_retriever 0.272734 True jigsaw_puzzle 0.041476 False
1571 794983741416415232 https://pbs.twimg.com/media/CvT6IV6WEAQhhV5.jpg 3 schipperke 0.363272 True kelpie 0.197021 True Norwegian_elkhound 0.151024 True
1579 796177847564038144 https://pbs.twimg.com/media/Cwx99rpW8AMk_Ie.jpg 1 golden_retriever 0.600276 True Labrador_retriever 0.140798 True seat_belt 0.087355 False
1588 798340744599797760 https://pbs.twimg.com/media/CrXhIqBW8AA6Bse.jpg 1 papillon 0.533180 True collie 0.192031 True Border_collie 0.121626 True
1589 798628517273620480 https://pbs.twimg.com/media/CUN4Or5UAAAa5K4.jpg 1 beagle 0.636169 True Labrador_retriever 0.119256 True golden_retriever 0.082549 True
1590 798644042770751489 https://pbs.twimg.com/media/CU3mITUWIAAfyQS.jpg 1 English_springer 0.403698 True Brittany_spaniel 0.347609 True Welsh_springer_spaniel 0.137186 True
1591 798665375516884993 https://pbs.twimg.com/media/CVMOlMiWwAA4Yxl.jpg 1 chow 0.243529 True hamster 0.227150 False Pomeranian 0.056057 True
... ... ... ... ... ... ... ... ... ... ... ... ...
1619 802624713319034886 https://pbs.twimg.com/media/CsrjryzWgAAZY00.jpg 1 cocker_spaniel 0.253442 True golden_retriever 0.162850 True otterhound 0.110921 True
1624 803692223237865472 https://pbs.twimg.com/media/CZhn-QAWwAASQan.jpg 1 Lakeland_terrier 0.530104 True Irish_terrier 0.197314 True Airedale 0.082515 True
1627 804413760345620481 https://pbs.twimg.com/media/CuRDF-XWcAIZSer.jpg 1 chow 0.090341 True binoculars 0.083499 False Irish_setter 0.077456 True
1634 805958939288408065 https://pbs.twimg.com/media/CtzKC7zXEAALfSo.jpg 1 Irish_setter 0.574557 True golden_retriever 0.339251 True seat_belt 0.046108 False
1636 806242860592926720 https://pbs.twimg.com/media/Ct72q9jWcAAhlnw.jpg 2 Cardigan 0.593858 True Shetland_sheepdog 0.130611 True Pembroke 0.100842 True
1640 807059379405148160 https://pbs.twimg.com/media/Ct2qO5PXEAE6eB0.jpg 1 seat_belt 0.474292 False golden_retriever 0.171393 True Labrador_retriever 0.110592 True
1645 808134635716833280 https://pbs.twimg.com/media/Cx5R8wPVEAALa9r.jpg 1 cocker_spaniel 0.740220 True Dandie_Dinmont 0.061604 True English_setter 0.041331 True
1652 809808892968534016 https://pbs.twimg.com/media/CwS4aqZXUAAe3IO.jpg 1 Labrador_retriever 0.861651 True golden_retriever 0.044462 True Staffordshire_bullterrier 0.016497 True
1683 813944609378369540 https://pbs.twimg.com/media/Cveg1-NXgAASaaT.jpg 1 Labrador_retriever 0.427742 True Great_Dane 0.190503 True curly-coated_retriever 0.146427 True
1693 816014286006976512 https://pbs.twimg.com/media/CiibOMzUYAA9Mxz.jpg 1 English_setter 0.677408 True Border_collie 0.052724 True cocker_spaniel 0.048572 True
1699 816829038950027264 https://pbs.twimg.com/media/CvoBPWRWgAA4het.jpg 1 dishwasher 0.700466 False golden_retriever 0.245773 True chow 0.039012 True
1703 817181837579653120 https://pbs.twimg.com/ext_tw_video_thumb/81596... 1 Tibetan_mastiff 0.506312 True Tibetan_terrier 0.295690 True otterhound 0.036251 True
1712 818588835076603904 https://pbs.twimg.com/media/Crwxb5yWgAAX5P_.jpg 1 Norwegian_elkhound 0.372202 True Chesapeake_Bay_retriever 0.137187 True malamute 0.071436 True
1717 819015331746349057 https://pbs.twimg.com/media/C12x-JTVIAAzdfl.jpg 4 prison 0.907083 False palace 0.020089 False umbrella 0.007850 False
1718 819015337530290176 https://pbs.twimg.com/media/C12whDoVEAALRxa.jpg 1 standard_poodle 0.351308 True toy_poodle 0.271929 True Tibetan_terrier 0.094759 True
1727 820446719150292993 https://pbs.twimg.com/media/CxqsX-8XUAAEvjD.jpg 3 golden_retriever 0.938048 True kuvasz 0.025119 True Labrador_retriever 0.022977 True
1736 821813639212650496 https://pbs.twimg.com/media/CtVAvX-WIAAcGTf.jpg 1 Saint_Bernard 0.995143 True Cardigan 0.003044 True English_springer 0.001050 True
1742 822647212903690241 https://pbs.twimg.com/media/C2oRbOuWEAAbVSl.jpg 1 Samoyed 0.416769 True malamute 0.252706 True kuvasz 0.157028 True
1746 823269594223824897 https://pbs.twimg.com/media/C2kzTGxWEAEOpPL.jpg 1 Samoyed 0.585441 True Pomeranian 0.193654 True Arctic_fox 0.071648 False
1755 824796380199809024 https://pbs.twimg.com/media/CwiuEJmW8AAZnit.jpg 2 gas_pump 0.676439 False harvester 0.049995 False swing 0.044660 False
1789 829878982036299777 https://pbs.twimg.com/media/C3nygbBWQAAjwcW.jpg 1 golden_retriever 0.617389 True Labrador_retriever 0.337053 True tennis_ball 0.008554 False
1803 832040443403784192 https://pbs.twimg.com/media/Cq9guJ5WgAADfpF.jpg 1 miniature_pinscher 0.796313 True Chihuahua 0.155413 True Staffordshire_bullterrier 0.030943 True
1804 832215726631055365 https://pbs.twimg.com/media/CwJR1okWIAA6XMp.jpg 1 Afghan_hound 0.274637 True borzoi 0.142204 True doormat 0.109677 False
1858 841833993020538882 https://pbs.twimg.com/ext_tw_video_thumb/81742... 1 ice_bear 0.336200 False Samoyed 0.201358 True Eskimo_dog 0.186789 True
1864 842892208864923648 https://pbs.twimg.com/ext_tw_video_thumb/80710... 1 Chihuahua 0.505370 True Pomeranian 0.120358 True toy_terrier 0.077008 True
1903 851953902622658560 https://pbs.twimg.com/media/C4KHj-nWQAA3poV.jpg 1 Staffordshire_bullterrier 0.757547 True American_Staffordshire_terrier 0.149950 True Chesapeake_Bay_retriever 0.047523 True
1944 861769973181624320 https://pbs.twimg.com/media/CzG425nWgAAnP7P.jpg 2 Arabian_camel 0.366248 False house_finch 0.209852 False cocker_spaniel 0.046403 True
1992 873697596434513921 https://pbs.twimg.com/media/DA7iHL5U0AA1OQo.jpg 1 laptop 0.153718 False French_bulldog 0.099984 True printer 0.077130 False
2041 885311592912609280 https://pbs.twimg.com/media/C4bTH6nWMAAX_bJ.jpg 1 Labrador_retriever 0.908703 True seat_belt 0.057091 False pug 0.011933 True
2055 888202515573088257 https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg 2 Pembroke 0.809197 True Rhodesian_ridgeback 0.054950 True beagle 0.038915 True

66 rows × 12 columns

In [48]:
## There are 66 duplicated columns for the jpg url
In [49]:
## Checking for common columns in df1 and imagedf1 columns
two_columns= pd.Series(list(df1)+list(imagedf1))
two_columns[two_columns.duplicated()]
Out[49]:
17    tweet_id
dtype: object
In [50]:
## tweet_id is the common column in both the dataset

Tweets_api

In [51]:
tweets_api.head(20)
Out[51]:
id favorite_count retweet_count
0 892420643555336193 39467 8853
1 892177421306343426 33819 6514
2 891815181378084864 25461 4328
3 891689557279858688 42908 8964
4 891327558926688256 41048 9774
5 891087950875897856 20562 3261
6 890971913173991426 12041 2158
7 890729181411237888 56848 16716
8 890609185150312448 28226 4429
9 890240255349198849 32467 7711
10 890006608113172480 31166 7624
11 889880896479866881 28268 5156
12 889665388333682689 38818 8538
13 889638837579907072 27672 4735
14 889531135344209921 15359 2321
15 889278841981685760 25652 5637
16 888917238123831296 29611 4709
17 888804989199671297 26080 4559
18 888554962724278272 20290 3732
19 888078434458587136 22201 3653
In [52]:
tweets_api.tail(20)
Out[52]:
id favorite_count retweet_count
2334 666273097616637952 184 82
2335 666268910803644416 108 37
2336 666104133288665088 14765 6871
2337 666102155909144576 81 16
2338 666099513787052032 164 73
2339 666094000022159362 169 79
2340 666082916733198337 121 47
2341 666073100786774016 335 174
2342 666071193221509120 154 67
2343 666063827256086533 496 232
2344 666058600524156928 115 61
2345 666057090499244032 304 146
2346 666055525042405380 448 261
2347 666051853826850816 1253 879
2348 666050758794694657 136 60
2349 666049248165822465 111 41
2350 666044226329800704 311 147
2351 666033412701032449 128 47
2352 666029285002620928 132 48
2353 666020888022790149 2535 532
In [53]:
tweets_api.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
id                2354 non-null int64
favorite_count    2354 non-null int64
retweet_count     2354 non-null int64
dtypes: int64(3)
memory usage: 55.2 KB
In [54]:
tweets_api.favorite_count.value_counts().sort_values()
Out[54]:
19163      1
1817       1
3864       1
262        1
1813       1
1096       1
9997       1
3852       1
1803       1
5897       1
3848       1
1799       1
1526       1
565        1
7938       1
857        1
8910       1
2441       1
20208      1
7916       1
1771       1
3818       1
9959       1
7908       1
1763       1
1761       1
28382      1
14081      1
10085      1
1208       1
        ... 
13755      2
1674       2
4578       2
2384       2
1074       2
8209       2
2906       2
327        2
3999       2
2046       2
20275      2
8945       2
2370       2
4228       2
2044       2
2979       2
1253       2
5792       2
592        2
496        2
931        2
610        3
345        3
2918       3
1339       3
2176       3
2768       3
2706       3
1691       3
0        179
Name: favorite_count, Length: 2007, dtype: int64
In [55]:
tweets_api['retweet_count'].value_counts().sort_values()
Out[55]:
274      1
2610     1
561      1
2608     1
4653     1
2602     1
479      1
1055     1
547      1
6690     1
4637     1
563      1
1059     1
537      1
535      1
27680    1
477      1
3130     1
475      1
11330    1
4533     1
433      1
429      1
2586     1
565      1
5159     1
366      1
4598     1
1149     1
285      1
        ..
231      3
888      3
119      3
896      3
135      3
836      3
792      3
776      3
426      3
4319     3
1225     3
183      4
146      4
61       4
748      4
2243     4
336      4
179      4
819      4
265      4
115      4
71       4
1124     4
542      4
577      4
516      4
1207     4
83       5
3652     5
1972     5
Name: retweet_count, Length: 1724, dtype: int64
In [56]:
sum(tweets_api['id'].duplicated())
Out[56]:
0
In [57]:
## Change the id column to tweet_id (only for remembering)
In [58]:
tweets_api.rename(columns={'id': 'tweet_id'},inplace=True)

Observations

Tideness issues

  1. There should only one table for imagedf1,tweet_json,df1(twitter_archive)
  2. The columns doggo,pupper,floofer, puppo should be one column (type)
Quality issues

twitter_archive(df1)

  1. Null Values (in_reply_to_status_id,in_reply_to_user_id, retweeted_status_user_id,retweeted_status_user_id,retweeted_status_timestamp ) as they are not required for analysis and they have a lot of null values
  2. Remove the 181 values in retweeted_status_id as we are only interested in original tweets
  3. Erroneous data timestamp (It is in string format)
  4. Wrong Denominator rating (There is denominator rating other than 10)
  5. The exapnded_url has some missing values, the url with missing values; means that there are no images and we should not be using the data with missing url's
  6. Incorrect dog names (vowels and other conjusction's in dog name)
  7. There are a lot of none values in dog type's (puppo,doggo,floofer,doggo,pupper)
  8. url not into human readible form

image_prediction(imagedf1)

  1. The column names p1,p2,p3 are not very informative
  2. The dog type have uppercase and lowercase names

Cleaning

During the cleaning process the first part is tackle the Tideness issues and then the quality issues

In [59]:
## Making the copy of the dataframes
df1_clean          = df1.copy()
imagedf1_clean     = imagedf1.copy()
tweets_api_clean   = tweets_api.copy()
In [60]:
##Checking
df1_clean.head()
Out[60]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name doggo floofer pupper puppo
0 892420643555336193 NaN NaN 2017-08-01 16:23:56 +0000 <a href="http://twitter.com/download/iphone" r... This is Phineas. He's a mystical boy. Only eve... NaN NaN NaN https://twitter.com/dog_rates/status/892420643... 13 10 Phineas None None None None
1 892177421306343426 NaN NaN 2017-08-01 00:17:27 +0000 <a href="http://twitter.com/download/iphone" r... This is Tilly. She's just checking pup on you.... NaN NaN NaN https://twitter.com/dog_rates/status/892177421... 13 10 Tilly None None None None
2 891815181378084864 NaN NaN 2017-07-31 00:18:03 +0000 <a href="http://twitter.com/download/iphone" r... This is Archie. He is a rare Norwegian Pouncin... NaN NaN NaN https://twitter.com/dog_rates/status/891815181... 12 10 Archie None None None None
3 891689557279858688 NaN NaN 2017-07-30 15:58:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Darla. She commenced a snooze mid meal... NaN NaN NaN https://twitter.com/dog_rates/status/891689557... 13 10 Darla None None None None
4 891327558926688256 NaN NaN 2017-07-29 16:00:24 +0000 <a href="http://twitter.com/download/iphone" r... This is Franklin. He would like you to stop ca... NaN NaN NaN https://twitter.com/dog_rates/status/891327558... 12 10 Franklin None None None None
In [61]:
imagedf1_clean.head(2)
Out[61]:
tweet_id jpg_url img_num p1 p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog
0 666020888022790149 https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg 1 Welsh_springer_spaniel 0.465074 True collie 0.156665 True Shetland_sheepdog 0.061428 True
1 666029285002620928 https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg 1 redbone 0.506826 True miniature_pinscher 0.074192 True Rhodesian_ridgeback 0.072010 True
In [62]:
tweets_api_clean.head()
Out[62]:
tweet_id favorite_count retweet_count
0 892420643555336193 39467 8853
1 892177421306343426 33819 6514
2 891815181378084864 25461 4328
3 891689557279858688 42908 8964
4 891327558926688256 41048 9774
Define
  1. Make the columns doggo,floofer, pupper, puppo into one column using pandas melt feature
Code
In [63]:
df1_clean= pd.melt(df1_clean,id_vars=['tweet_id','in_reply_to_status_id','in_reply_to_user_id','timestamp','source','text','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp'
                   ,'expanded_urls','rating_numerator','rating_denominator','name'] ,
        value_vars=['doggo','floofer','pupper','puppo'],var_name='stages',value_name='dog_stages')
In [64]:
df1_clean.drop('stages',axis=1,inplace=True)
In [65]:
### Checking for duplicated values
sum(df1_clean.duplicated())
Out[65]:
6674
In [66]:
### Removing duplicated values 
df1_clean.drop_duplicates(inplace=True)
Testing
In [67]:
df1_clean.shape
Out[67]:
(2750, 14)
In [68]:
df1_clean.head()
Out[68]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name dog_stages
0 892420643555336193 NaN NaN 2017-08-01 16:23:56 +0000 <a href="http://twitter.com/download/iphone" r... This is Phineas. He's a mystical boy. Only eve... NaN NaN NaN https://twitter.com/dog_rates/status/892420643... 13 10 Phineas None
1 892177421306343426 NaN NaN 2017-08-01 00:17:27 +0000 <a href="http://twitter.com/download/iphone" r... This is Tilly. She's just checking pup on you.... NaN NaN NaN https://twitter.com/dog_rates/status/892177421... 13 10 Tilly None
2 891815181378084864 NaN NaN 2017-07-31 00:18:03 +0000 <a href="http://twitter.com/download/iphone" r... This is Archie. He is a rare Norwegian Pouncin... NaN NaN NaN https://twitter.com/dog_rates/status/891815181... 12 10 Archie None
3 891689557279858688 NaN NaN 2017-07-30 15:58:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Darla. She commenced a snooze mid meal... NaN NaN NaN https://twitter.com/dog_rates/status/891689557... 13 10 Darla None
4 891327558926688256 NaN NaN 2017-07-29 16:00:24 +0000 <a href="http://twitter.com/download/iphone" r... This is Franklin. He would like you to stop ca... NaN NaN NaN https://twitter.com/dog_rates/status/891327558... 12 10 Franklin None
In [69]:
df1_clean.sample(5)
Out[69]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name dog_stages
2189 668967877119254528 6.689207e+17 21435658.0 2015-11-24 01:42:25 +0000 <a href="http://twitter.com/download/iphone" r... 12/10 good shit Bubka\n@wane15 NaN NaN NaN NaN 12 10 None None
971 750101899009982464 NaN NaN 2016-07-04 23:00:03 +0000 <a href="http://twitter.com/download/iphone" r... Meet Lilah. She agreed on one quick pic. Now s... NaN NaN NaN https://twitter.com/dog_rates/status/750101899... 11 10 Lilah None
682 788552643979468800 NaN NaN 2016-10-19 01:29:35 +0000 <a href="http://twitter.com/download/iphone" r... RT @dog_rates: Say hello to mad pupper. You kn... 7.363926e+17 4.196984e+09 2016-05-28 03:04:00 +0000 https://vine.co/v/iEggaEOiLO3,https://vine.co/... 13 10 mad None
1181 719332531645071360 NaN NaN 2016-04-11 01:13:34 +0000 <a href="http://twitter.com/download/iphone" r... This is Pippa. She managed to start the car bu... NaN NaN NaN https://twitter.com/dog_rates/status/719332531... 11 10 Pippa None
1739 679511351870550016 NaN NaN 2015-12-23 03:58:25 +0000 <a href="http://twitter.com/download/iphone" r... Say hello to William. He makes fun of others b... NaN NaN NaN https://twitter.com/dog_rates/status/679511351... 7 10 William None

Define

  1. Merge all the dataframes and make them into one dataframe name it as twitter_master_clean
Code
In [70]:
## Creating an abc dataframe by merging df1_clean and imagedf1_clean
abc =pd.merge(df1_clean,imagedf1_clean,on='tweet_id',how='inner')
In [71]:
### Merging the abc dataframe with tweet_json to make on dataframe twitter_master_clean
twitter_master_clean = pd.merge(abc,tweets_api_clean,on='tweet_id',how='inner')
Testing
In [72]:
abc.shape
Out[72]:
(2409, 25)
In [73]:
abc.columns
Out[73]:
Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name', 'dog_stages', 'jpg_url', 'img_num', 'p1',
       'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',
       'p3_dog'],
      dtype='object')
In [74]:
twitter_master_clean.head()
Out[74]:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls ... p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog favorite_count retweet_count
0 892420643555336193 NaN NaN 2017-08-01 16:23:56 +0000 <a href="http://twitter.com/download/iphone" r... This is Phineas. He's a mystical boy. Only eve... NaN NaN NaN https://twitter.com/dog_rates/status/892420643... ... 0.097049 False bagel 0.085851 False banana 0.076110 False 39467 8853
1 892177421306343426 NaN NaN 2017-08-01 00:17:27 +0000 <a href="http://twitter.com/download/iphone" r... This is Tilly. She's just checking pup on you.... NaN NaN NaN https://twitter.com/dog_rates/status/892177421... ... 0.323581 True Pekinese 0.090647 True papillon 0.068957 True 33819 6514
2 891815181378084864 NaN NaN 2017-07-31 00:18:03 +0000 <a href="http://twitter.com/download/iphone" r... This is Archie. He is a rare Norwegian Pouncin... NaN NaN NaN https://twitter.com/dog_rates/status/891815181... ... 0.716012 True malamute 0.078253 True kelpie 0.031379 True 25461 4328
3 891689557279858688 NaN NaN 2017-07-30 15:58:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Darla. She commenced a snooze mid meal... NaN NaN NaN https://twitter.com/dog_rates/status/891689557... ... 0.170278 False Labrador_retriever 0.168086 True spatula 0.040836 False 42908 8964
4 891327558926688256 NaN NaN 2017-07-29 16:00:24 +0000 <a href="http://twitter.com/download/iphone" r... This is Franklin. He would like you to stop ca... NaN NaN NaN https://twitter.com/dog_rates/status/891327558... ... 0.555712 True English_springer 0.225770 True German_short-haired_pointer 0.175219 True 41048 9774

5 rows × 27 columns

In [75]:
twitter_master_clean.columns
Out[75]:
Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name', 'dog_stages', 'jpg_url', 'img_num', 'p1',
       'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',
       'p3_dog', 'favorite_count', 'retweet_count'],
      dtype='object')
In [76]:
twitter_master_clean.shape
Out[76]:
(2406, 27)

Quality

Define

Filter the tweet_master_clean dataframe to remove the 181 values in retweeted_status_id as we are only interested in original tweets

Code

In [77]:
## Checting for null values. Instead of 181, 95 values are left because of the merge operations
sum(twitter_master_clean.retweeted_status_id.notnull())
Out[77]:
95
In [78]:
twitter_master_clean.drop(twitter_master_clean[twitter_master_clean.retweeted_status_id.notnull()].index,inplace=True)

Testing

In [79]:
twitter_master_clean.shape
Out[79]:
(2311, 27)
In [80]:
sum(twitter_master_clean.retweeted_status_id.notnull())
Out[80]:
0

Define

  1. Dropping the columns not required for ananlysis ( (in_reply_to_status_id,in_reply_to_user_id, retweeted_status_user_id,retweeted_status_user_id,retweeted_status_timestamp )
In [81]:
#### Code
In [82]:
twitter_master_clean.columns
Out[82]:
Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name', 'dog_stages', 'jpg_url', 'img_num', 'p1',
       'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',
       'p3_dog', 'favorite_count', 'retweet_count'],
      dtype='object')
In [83]:
twitter_master_clean.drop(['in_reply_to_status_id','in_reply_to_user_id', 'retweeted_status_user_id',
                           'retweeted_status_user_id','retweeted_status_timestamp'],axis=1,inplace=True)
In [84]:
## Dropping retweeted_status_id
twitter_master_clean.drop('retweeted_status_id',axis=1,inplace=True)
In [85]:
#### Testing
twitter_master_clean.columns
Out[85]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
       'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
       'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf',
       'p2_dog', 'p3', 'p3_conf', 'p3_dog', 'favorite_count', 'retweet_count'],
      dtype='object')
In [86]:
twitter_master_clean.head()
Out[86]:
tweet_id timestamp source text expanded_urls rating_numerator rating_denominator name dog_stages jpg_url ... p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog favorite_count retweet_count
0 892420643555336193 2017-08-01 16:23:56 +0000 <a href="http://twitter.com/download/iphone" r... This is Phineas. He's a mystical boy. Only eve... https://twitter.com/dog_rates/status/892420643... 13 10 Phineas None https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg ... 0.097049 False bagel 0.085851 False banana 0.076110 False 39467 8853
1 892177421306343426 2017-08-01 00:17:27 +0000 <a href="http://twitter.com/download/iphone" r... This is Tilly. She's just checking pup on you.... https://twitter.com/dog_rates/status/892177421... 13 10 Tilly None https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg ... 0.323581 True Pekinese 0.090647 True papillon 0.068957 True 33819 6514
2 891815181378084864 2017-07-31 00:18:03 +0000 <a href="http://twitter.com/download/iphone" r... This is Archie. He is a rare Norwegian Pouncin... https://twitter.com/dog_rates/status/891815181... 12 10 Archie None https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg ... 0.716012 True malamute 0.078253 True kelpie 0.031379 True 25461 4328
3 891689557279858688 2017-07-30 15:58:51 +0000 <a href="http://twitter.com/download/iphone" r... This is Darla. She commenced a snooze mid meal... https://twitter.com/dog_rates/status/891689557... 13 10 Darla None https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg ... 0.170278 False Labrador_retriever 0.168086 True spatula 0.040836 False 42908 8964
4 891327558926688256 2017-07-29 16:00:24 +0000 <a href="http://twitter.com/download/iphone" r... This is Franklin. He would like you to stop ca... https://twitter.com/dog_rates/status/891327558... 12 10 Franklin None https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg ... 0.555712 True English_springer 0.225770 True German_short-haired_pointer 0.175219 True 41048 9774

5 rows × 22 columns

Define

  1. Converting timestamp into date and time format from string
Code
In [87]:
twitter_master_clean['timestamp'] = pd.to_datetime(twitter_master_clean['timestamp'])

Testing

In [88]:
type(twitter_master_clean['timestamp'][0])
Out[88]:
pandas._libs.tslibs.timestamps.Timestamp
In [89]:
twitter_master_clean.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2311 entries, 0 to 2405
Data columns (total 22 columns):
tweet_id              2311 non-null int64
timestamp             2311 non-null datetime64[ns]
source                2311 non-null object
text                  2311 non-null object
expanded_urls         2311 non-null object
rating_numerator      2311 non-null int64
rating_denominator    2311 non-null int64
name                  2311 non-null object
dog_stages            2311 non-null object
jpg_url               2311 non-null object
img_num               2311 non-null int64
p1                    2311 non-null object
p1_conf               2311 non-null float64
p1_dog                2311 non-null bool
p2                    2311 non-null object
p2_conf               2311 non-null float64
p2_dog                2311 non-null bool
p3                    2311 non-null object
p3_conf               2311 non-null float64
p3_dog                2311 non-null bool
favorite_count        2311 non-null int64
retweet_count         2311 non-null int64
dtypes: bool(3), datetime64[ns](1), float64(3), int64(6), object(9)
memory usage: 447.9+ KB

Define

  1. Create the list of incorrect name entries and replace them with 'NG'(Not_Given) and then converting every name into lowercase

Code

In [90]:
name_1 = ['his','a','the','an','quite','such','None']
for name in name_1:
    twitter_master_clean['name'].replace(name,'NG',inplace=True)
In [91]:
### Converting all the names in lower case
twitter_master_clean['name']=twitter_master_clean.name.str.lower()

Testing

In [92]:
twitter_master_clean['name'].head()
Out[92]:
0     phineas
1       tilly
2      archie
3       darla
4    franklin
Name: name, dtype: object
In [93]:
twitter_master_clean['name'].tail()
Out[93]:
2401    ng
2402    ng
2403    ng
2404    ng
2405    ng
Name: name, dtype: object
In [94]:
twitter_master_clean.name.sample(5)
Out[94]:
1729        hector
1807            ng
2123      clarence
1130            ng
865     brandonald
Name: name, dtype: object

Define

Replacing the none values in dog_stages np.nan

Code

In [95]:
twitter_master_clean.dog_stages.unique()
Out[95]:
array(['None', 'doggo', 'puppo', 'pupper', 'floofer'], dtype=object)
In [96]:
twitter_master_clean.dog_stages.replace('None',np.nan,inplace=True)

Testing

In [97]:
twitter_master_clean.dog_stages.value_counts()
Out[97]:
pupper     212
doggo       74
puppo       23
floofer      8
Name: dog_stages, dtype: int64
In [98]:
twitter_master_clean.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2311 entries, 0 to 2405
Data columns (total 22 columns):
tweet_id              2311 non-null int64
timestamp             2311 non-null datetime64[ns]
source                2311 non-null object
text                  2311 non-null object
expanded_urls         2311 non-null object
rating_numerator      2311 non-null int64
rating_denominator    2311 non-null int64
name                  2311 non-null object
dog_stages            317 non-null object
jpg_url               2311 non-null object
img_num               2311 non-null int64
p1                    2311 non-null object
p1_conf               2311 non-null float64
p1_dog                2311 non-null bool
p2                    2311 non-null object
p2_conf               2311 non-null float64
p2_dog                2311 non-null bool
p3                    2311 non-null object
p3_conf               2311 non-null float64
p3_dog                2311 non-null bool
favorite_count        2311 non-null int64
retweet_count         2311 non-null int64
dtypes: bool(3), datetime64[ns](1), float64(3), int64(6), object(9)
memory usage: 447.9+ KB

Define

  1. Extract the source from the source url and add it in human readible form

Code

In [99]:
twitter_master_clean.source.value_counts()
Out[99]:
<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>     2269
<a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>                       29
<a href="https://about.twitter.com/products/tweetdeck" rel="nofollow">TweetDeck</a>      13
Name: source, dtype: int64
In [100]:
twitter_master_clean['source']=twitter_master_clean.source.apply(lambda i : re.findall(r'>(.*)<',i)[0])

Testing

In [101]:
twitter_master_clean.source.value_counts()
Out[101]:
Twitter for iPhone    2269
Twitter Web Client      29
TweetDeck               13
Name: source, dtype: int64

Define

  1. Filter the values that does not have denomitor equal to 10 and then check the details in the text column and correct the values in rating_denominator column

Code

In [102]:
twitter_master_clean[twitter_master_clean.rating_denominator != 10]
Out[102]:
tweet_id timestamp source text expanded_urls rating_numerator rating_denominator name dog_stages jpg_url ... p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog favorite_count retweet_count
407 820690176645140481 2017-01-15 17:52:40 Twitter for iPhone The floofs have been released I repeat the flo... https://twitter.com/dog_rates/status/820690176... 84 70 ng NaN https://pbs.twimg.com/media/C2OtWr0VQAEnS9r.jpg ... 0.872064 True kuvasz 0.059526 True Samoyed 0.037400 True 13518 3716
495 810984652412424192 2016-12-19 23:06:23 Twitter for iPhone Meet Sam. She smiles 24/7 &amp; secretly aspir... https://www.gofundme.com/sams-smile,https://tw... 24 7 sam NaN https://pbs.twimg.com/media/C0EyPZbXAAAceSc.jpg ... 0.871342 True Tibetan_mastiff 0.036708 True Labrador_retriever 0.025823 True 5927 1655
866 758467244762497024 2016-07-28 01:00:57 Twitter for iPhone Why does this never happen at my front door...... https://twitter.com/dog_rates/status/758467244... 165 150 ng NaN https://pbs.twimg.com/ext_tw_video_thumb/75846... ... 0.436377 True Chihuahua 0.113956 True American_Staffordshire_terrier 0.099689 True 5316 2539
1044 740373189193256964 2016-06-08 02:41:38 Twitter for iPhone After so many requests, this is Bretagne. She ... https://twitter.com/dog_rates/status/740373189... 9 11 ng NaN https://pbs.twimg.com/media/CkZVdJ6WYAAXZ5A.jpg ... 0.807644 True kuvasz 0.101286 True Labrador_retriever 0.023785 True 20648 9220
1104 731156023742988288 2016-05-13 16:15:54 Twitter for iPhone Say hello to this unbelievably well behaved sq... https://twitter.com/dog_rates/status/731156023... 204 170 this NaN https://pbs.twimg.com/media/CiWWhVNUYAAab_r.jpg ... 0.501767 False breakwater 0.051351 False king_penguin 0.049444 False 4196 1434
1157 722974582966214656 2016-04-21 02:25:47 Twitter for iPhone Happy 4/20 from the squad! 13/10 for all https... https://twitter.com/dog_rates/status/722974582... 4 20 ng NaN https://pbs.twimg.com/media/CgiFjIpWgAA4wVp.jpg ... 0.246762 True Greater_Swiss_Mountain_dog 0.126131 True Weimaraner 0.085297 True 4493 1764
1197 716439118184652801 2016-04-03 01:36:11 Twitter for iPhone This is Bluebert. He just saw that both #Final... https://twitter.com/dog_rates/status/716439118... 50 50 bluebert NaN https://pbs.twimg.com/media/CfFNk7cWAAA-hND.jpg ... 0.396495 True malamute 0.317053 True Eskimo_dog 0.273419 True 2574 247
1219 713900603437621249 2016-03-27 01:29:02 Twitter for iPhone Happy Saturday here's 9 puppers on a bench. 99... https://twitter.com/dog_rates/status/713900603... 99 90 ng NaN https://pbs.twimg.com/media/CehIzzZWQAEyHH5.jpg ... 0.371816 True cocker_spaniel 0.177413 True Irish_setter 0.092725 True 3062 829
1246 710658690886586372 2016-03-18 02:46:49 Twitter for iPhone Here's a brigade of puppers. All look very pre... https://twitter.com/dog_rates/status/710658690... 80 80 ng NaN https://pbs.twimg.com/media/CdzETn4W4AAVU5N.jpg ... 0.948617 True Dandie_Dinmont 0.018664 True cairn 0.015943 True 2529 636
1265 709198395643068416 2016-03-14 02:04:08 Twitter for iPhone From left to right:\nCletus, Jerome, Alejandro... https://twitter.com/dog_rates/status/709198395... 45 50 ng NaN https://pbs.twimg.com/media/CdeUKpcWoAAJAWJ.jpg ... 0.490783 True wire-haired_fox_terrier 0.083513 True English_setter 0.083184 True 2634 721
1341 704054845121142784 2016-02-28 21:25:30 Twitter for iPhone Here is a whole flock of puppers. 60/50 I'll ... https://twitter.com/dog_rates/status/704054845... 60 50 ng NaN https://pbs.twimg.com/media/CcVOJEcXEAM0FHL.jpg ... 0.667939 True kuvasz 0.228764 True golden_retriever 0.043885 True 3201 1028
1430 697463031882764288 2016-02-10 16:51:59 Twitter for iPhone Happy Wednesday here's a bucket of pups. 44/40... https://twitter.com/dog_rates/status/697463031... 44 40 ng NaN https://pbs.twimg.com/media/Ca3i7CzXIAMLhg8.jpg ... 0.999885 True golden_retriever 0.000098 True pug 0.000008 True 3748 1552
1641 684225744407494656 2016-01-05 04:11:44 Twitter for iPhone Two sneaky puppers were not initially seen, mo... https://twitter.com/dog_rates/status/684225744... 143 130 ng NaN https://pbs.twimg.com/media/CX7br3HWsAAQ9L1.jpg ... 0.203249 True Samoyed 0.067958 True Great_Pyrenees 0.065327 True 1369 239
1642 684222868335505415 2016-01-05 04:00:18 Twitter for iPhone Someone help the girl is being mugged. Several... https://twitter.com/dog_rates/status/684222868... 121 110 ng NaN https://pbs.twimg.com/media/CX7Y_ByWwAEJdUy.jpg ... 0.791182 True cocker_spaniel 0.072444 True teddy 0.071486 False 4225 1563
1674 682962037429899265 2016-01-01 16:30:13 Twitter for iPhone This is Darrel. He just robbed a 7/11 and is i... https://twitter.com/dog_rates/status/682962037... 7 11 darrel NaN https://pbs.twimg.com/media/CXpeVzQW8AApKYb.jpg ... 0.278600 False Chihuahua 0.155207 True loupe 0.153598 False 26239 15043
1802 677716515794329600 2015-12-18 05:06:23 Twitter for iPhone IT'S PUPPERGEDDON. Total of 144/120 ...I think... https://twitter.com/dog_rates/status/677716515... 144 120 ng NaN https://pbs.twimg.com/media/CWe7kw9W4AE8UJh.jpg ... 0.662908 False crib 0.031891 False chow 0.025438 True 3323 1104
1871 675853064436391936 2015-12-13 01:41:41 Twitter for iPhone Here we have an entire platoon of puppers. Tot... https://twitter.com/dog_rates/status/675853064... 88 80 ng NaN https://pbs.twimg.com/media/CWEcxqWVEAAHyGH.jpg ... 0.868367 True golden_retriever 0.043305 True vizsla 0.028207 True 2927 1460
2385 666287406224695296 2015-11-16 16:11:11 Twitter for iPhone This is an Albanian 3 1/2 legged Episcopalian... https://twitter.com/dog_rates/status/666287406... 1 2 ng NaN https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg ... 0.857531 True toy_poodle 0.063064 True miniature_poodle 0.025581 True 152 71

18 rows × 22 columns

In [103]:
d= twitter_master_clean.copy()
In [104]:
for n in twitter_master_clean['rating_denominator']:
    if n > 10 or n < 10:
        twitter_master_clean['rating_denominator'].replace(n,10,inplace=True)
    else:
        twitter_master_clean['rating_denominator']

Testing

In [105]:
twitter_master_clean[twitter_master_clean.rating_denominator != 10]
Out[105]:
tweet_id timestamp source text expanded_urls rating_numerator rating_denominator name dog_stages jpg_url ... p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog favorite_count retweet_count

0 rows × 22 columns

In [106]:
twitter_master_clean.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2311 entries, 0 to 2405
Data columns (total 22 columns):
tweet_id              2311 non-null int64
timestamp             2311 non-null datetime64[ns]
source                2311 non-null object
text                  2311 non-null object
expanded_urls         2311 non-null object
rating_numerator      2311 non-null int64
rating_denominator    2311 non-null int64
name                  2311 non-null object
dog_stages            317 non-null object
jpg_url               2311 non-null object
img_num               2311 non-null int64
p1                    2311 non-null object
p1_conf               2311 non-null float64
p1_dog                2311 non-null bool
p2                    2311 non-null object
p2_conf               2311 non-null float64
p2_dog                2311 non-null bool
p3                    2311 non-null object
p3_conf               2311 non-null float64
p3_dog                2311 non-null bool
favorite_count        2311 non-null int64
retweet_count         2311 non-null int64
dtypes: bool(3), datetime64[ns](1), float64(3), int64(6), object(9)
memory usage: 447.9+ KB
In [107]:
### Checking if the shape of twitter_master_clean values ==10 has same number of row values
twitter_master_clean[twitter_master_clean.rating_denominator == 10].shape[0]
Out[107]:
2311

Define

Change the type of tweet_id from int64 to string because the id's should be string and we won't be using it for analysis

Code

In [108]:
type(twitter_master_clean.tweet_id[0])
Out[108]:
numpy.int64
In [109]:
twitter_master_clean.tweet_id = twitter_master_clean.tweet_id.astype('str')

Testing

In [110]:
type(twitter_master_clean.tweet_id[0])
Out[110]:
str

Define

Change the names in p1, p2, p3, in lower case

Code

In [111]:
s=['p1','p2','p3']
for n in s:
    twitter_master_clean[n]=twitter_master_clean[n].str.lower()

Testing

In [112]:
twitter_master_clean.p1.head()
Out[112]:
0         orange
1      chihuahua
2      chihuahua
3    paper_towel
4         basset
Name: p1, dtype: object
In [113]:
twitter_master_clean.p2.head()
Out[113]:
0                 bagel
1              pekinese
2              malamute
3    labrador_retriever
4      english_springer
Name: p2, dtype: object
In [114]:
twitter_master_clean.p3.head()
Out[114]:
0                         banana
1                       papillon
2                         kelpie
3                        spatula
4    german_short-haired_pointer
Name: p3, dtype: object

Define

Change the names of p1, p1_conf,p1_dog,p2_conf,p2_dog,p3,p3_conf,p3_dog

Code

In [115]:
twitter_master_clean.rename(columns={'p1':'first_predict_breed','p1_conf':'prediction1_confidence','p1_dog':'predict1_result'
                                    ,'p2':'second_predict_breed','p2_conf':'prediction2_confidence','p2_dog':'predict2_result',
                                    'p3':'third_predict_breed','p3_conf':'prediction3_confidence','p3_dog':'predict3_result'}
                                       ,inplace=True)

Testing

In [116]:
twitter_master_clean.columns
Out[116]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
       'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
       'jpg_url', 'img_num', 'first_predict_breed', 'prediction1_confidence',
       'predict1_result', 'second_predict_breed', 'prediction2_confidence',
       'predict2_result', 'third_predict_breed', 'prediction3_confidence',
       'predict3_result', 'favorite_count', 'retweet_count'],
      dtype='object')

Define

Extract month,day,year from timestamp column

Code

In [117]:
### Extracting year
twitter_master_clean['year'] = twitter_master_clean['timestamp'].dt.year
In [118]:
### Extracting month
twitter_master_clean['month'] = twitter_master_clean['timestamp'].dt.month
In [119]:
### Extracting day
twitter_master_clean['day'] = twitter_master_clean['timestamp'].dt.day
In [120]:
### Extracting time
twitter_master_clean['time'] = twitter_master_clean['timestamp'].dt.time
In [121]:
### Extracting day_of_week
twitter_master_clean['day_of_week'] = twitter_master_clean['timestamp'].dt.dayofweek
days = {0:'Mon',1:'Tues',2:'Weds',3:'Thurs',4:'Fri',5:'Sat',6:'Sun'}
twitter_master_clean['day_of_week'] = twitter_master_clean['day_of_week'].apply(lambda x: days[x])

Testing

In [122]:
twitter_master_clean[['year','month','day','time','day_of_week']].head()
Out[122]:
year month day time day_of_week
0 2017 8 1 16:23:56 Tues
1 2017 8 1 00:17:27 Tues
2 2017 7 31 00:18:03 Mon
3 2017 7 30 15:58:51 Sun
4 2017 7 29 16:00:24 Sat

Storing

In [123]:
#### Writing the cleaned twitter_master_clean into a csvfile
twitter_master_clean.to_csv('twitter_archive_master.csv',encoding='utf-8',index=False)

Analysis

In [124]:
### Reading the master file
twitter_archive_master = pd.read_csv("twitter_archive_master.csv")
In [125]:
twitter_archive_master.head()
Out[125]:
tweet_id timestamp source text expanded_urls rating_numerator rating_denominator name dog_stages jpg_url ... third_predict_breed prediction3_confidence predict3_result favorite_count retweet_count year month day time day_of_week
0 892420643555336193 2017-08-01 16:23:56 Twitter for iPhone This is Phineas. He's a mystical boy. Only eve... https://twitter.com/dog_rates/status/892420643... 13 10 phineas NaN https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg ... banana 0.076110 False 39467 8853 2017 8 1 16:23:56 Tues
1 892177421306343426 2017-08-01 00:17:27 Twitter for iPhone This is Tilly. She's just checking pup on you.... https://twitter.com/dog_rates/status/892177421... 13 10 tilly NaN https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg ... papillon 0.068957 True 33819 6514 2017 8 1 00:17:27 Tues
2 891815181378084864 2017-07-31 00:18:03 Twitter for iPhone This is Archie. He is a rare Norwegian Pouncin... https://twitter.com/dog_rates/status/891815181... 12 10 archie NaN https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg ... kelpie 0.031379 True 25461 4328 2017 7 31 00:18:03 Mon
3 891689557279858688 2017-07-30 15:58:51 Twitter for iPhone This is Darla. She commenced a snooze mid meal... https://twitter.com/dog_rates/status/891689557... 13 10 darla NaN https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg ... spatula 0.040836 False 42908 8964 2017 7 30 15:58:51 Sun
4 891327558926688256 2017-07-29 16:00:24 Twitter for iPhone This is Franklin. He would like you to stop ca... https://twitter.com/dog_rates/status/891327558... 12 10 franklin NaN https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg ... german_short-haired_pointer 0.175219 True 41048 9774 2017 7 29 16:00:24 Sat

5 rows × 27 columns

In [126]:
### Analyze the source for sending the twitter feeds using countplot
In [127]:
sort=twitter_archive_master['source'].value_counts().index
In [128]:
colours = ["maroon", "navy", "gold"]
sns.set(style='whitegrid')
sns.countplot(data=twitter_archive_master,y='source',order=sort,palette=colours)
plt.xticks(rotation=360)
plt.xlabel("Count",fontsize=12)
plt.ylabel("Source",fontsize=12)
plt.title("SourceCount",fontsize=17)
plt.show()

Here we can see the most number of twitter updates are done using i-phone app and then people use the Twitter web_client

In [129]:
## Filtter the numerator that has values less than 15
In [130]:
d2= twitter_archive_master[twitter_archive_master['rating_numerator'] <=15]
In [131]:
d2.head()
Out[131]:
tweet_id timestamp source text expanded_urls rating_numerator rating_denominator name dog_stages jpg_url ... third_predict_breed prediction3_confidence predict3_result favorite_count retweet_count year month day time day_of_week
0 892420643555336193 2017-08-01 16:23:56 Twitter for iPhone This is Phineas. He's a mystical boy. Only eve... https://twitter.com/dog_rates/status/892420643... 13 10 phineas NaN https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg ... banana 0.076110 False 39467 8853 2017 8 1 16:23:56 Tues
1 892177421306343426 2017-08-01 00:17:27 Twitter for iPhone This is Tilly. She's just checking pup on you.... https://twitter.com/dog_rates/status/892177421... 13 10 tilly NaN https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg ... papillon 0.068957 True 33819 6514 2017 8 1 00:17:27 Tues
2 891815181378084864 2017-07-31 00:18:03 Twitter for iPhone This is Archie. He is a rare Norwegian Pouncin... https://twitter.com/dog_rates/status/891815181... 12 10 archie NaN https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg ... kelpie 0.031379 True 25461 4328 2017 7 31 00:18:03 Mon
3 891689557279858688 2017-07-30 15:58:51 Twitter for iPhone This is Darla. She commenced a snooze mid meal... https://twitter.com/dog_rates/status/891689557... 13 10 darla NaN https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg ... spatula 0.040836 False 42908 8964 2017 7 30 15:58:51 Sun
4 891327558926688256 2017-07-29 16:00:24 Twitter for iPhone This is Franklin. He would like you to stop ca... https://twitter.com/dog_rates/status/891327558... 12 10 franklin NaN https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg ... german_short-haired_pointer 0.175219 True 41048 9774 2017 7 29 16:00:24 Sat

5 rows × 27 columns

In [132]:
### Calculating the ratio of popuarity
In [133]:
d2['popularity']=d2['rating_numerator']/d2['rating_denominator']
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.
In [134]:
d2.head()
Out[134]:
tweet_id timestamp source text expanded_urls rating_numerator rating_denominator name dog_stages jpg_url ... prediction3_confidence predict3_result favorite_count retweet_count year month day time day_of_week popularity
0 892420643555336193 2017-08-01 16:23:56 Twitter for iPhone This is Phineas. He's a mystical boy. Only eve... https://twitter.com/dog_rates/status/892420643... 13 10 phineas NaN https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg ... 0.076110 False 39467 8853 2017 8 1 16:23:56 Tues 1.3
1 892177421306343426 2017-08-01 00:17:27 Twitter for iPhone This is Tilly. She's just checking pup on you.... https://twitter.com/dog_rates/status/892177421... 13 10 tilly NaN https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg ... 0.068957 True 33819 6514 2017 8 1 00:17:27 Tues 1.3
2 891815181378084864 2017-07-31 00:18:03 Twitter for iPhone This is Archie. He is a rare Norwegian Pouncin... https://twitter.com/dog_rates/status/891815181... 12 10 archie NaN https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg ... 0.031379 True 25461 4328 2017 7 31 00:18:03 Mon 1.2
3 891689557279858688 2017-07-30 15:58:51 Twitter for iPhone This is Darla. She commenced a snooze mid meal... https://twitter.com/dog_rates/status/891689557... 13 10 darla NaN https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg ... 0.040836 False 42908 8964 2017 7 30 15:58:51 Sun 1.3
4 891327558926688256 2017-07-29 16:00:24 Twitter for iPhone This is Franklin. He would like you to stop ca... https://twitter.com/dog_rates/status/891327558... 12 10 franklin NaN https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg ... 0.175219 True 41048 9774 2017 7 29 16:00:24 Sat 1.2

5 rows × 28 columns

In [135]:
### Analyzing the most famous dog breed in firstprediction
group1 = d2.groupby(['first_predict_breed'],as_index=False).agg({'popularity' :['max']})
In [136]:
d2.columns
Out[136]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
       'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
       'jpg_url', 'img_num', 'first_predict_breed', 'prediction1_confidence',
       'predict1_result', 'second_predict_breed', 'prediction2_confidence',
       'predict2_result', 'third_predict_breed', 'prediction3_confidence',
       'predict3_result', 'favorite_count', 'retweet_count', 'year', 'month',
       'day', 'time', 'day_of_week', 'popularity'],
      dtype='object')
In [137]:
group1.columns =group1.columns.droplevel(level=1)
In [138]:
group1.sort_values('popularity',ascending=False,inplace=True)
In [139]:
group1.replace('laptop','lapdog',inplace=True)
In [140]:
a = group1.sample(20)
In [141]:
a.sort_values('popularity',ascending=False,inplace=True)
In [142]:
plt.figure(figsize=[20.5,13.5])
sns.barplot(data=a,x='first_predict_breed',y='popularity')
plt.xticks(rotation=45)
plt.xlabel('Type1Breed')
plt.ylabel('Popularity')
plt.title('Type1_Popularity')
Out[142]:
Text(0.5,1,'Type1_Popularity')

The graph above shows the graph of the sample rating from this we can know that rating range's as high as 1.3 to as low apprx 0.2 The values will change as this is based on sample data just to show case the rating trend of the dogs

In [143]:
### Analyzing the number of retweet_count and favourite_count based upon year
plt.figure(figsize=[20.5,13.5])
sns.scatterplot(data=twitter_archive_master,x='favorite_count',y='retweet_count',hue='year')
plt.xlabel('Favourite_count',fontsize=12)
plt.ylabel('Retweet_count',fontsize=12)
plt.title('Favourite Vs Retweet_count',fontsize=17)
plt.show()

Here we can see relationship between facourite_count and retweet_Count. The relationship is positive. We can also see that most of the values are for all the years are 20000. The maximum value for retweets is 80,000 and maximum value for favourite_count is around 130000

In [144]:
### Analyzing by month
group3= twitter_archive_master.groupby(by=['year','month'],as_index=False).agg({'tweet_id': ['count']})
In [145]:
group3.columns =group3.columns.droplevel(level=1)
In [146]:
group3
Out[146]:
year month tweet_id
0 2015 11 296
1 2015 12 430
2 2016 1 210
3 2016 2 131
4 2016 3 132
5 2016 4 65
6 2016 5 72
7 2016 6 96
8 2016 7 114
9 2016 8 63
10 2016 9 75
11 2016 10 73
12 2016 11 68
13 2016 12 64
14 2017 1 83
15 2017 2 79
16 2017 3 54
17 2017 4 47
18 2017 5 48
19 2017 6 52
20 2017 7 57
21 2017 8 2
In [147]:
plt.figure(figsize=[20.5,13.5])
sns.relplot(data=group3,x='month',y='tweet_id',kind='line',col='year')
Out[147]:
<seaborn.axisgrid.FacetGrid at 0x7f95b1548b38>
<matplotlib.figure.Figure at 0x7f95b1571da0>

Here we can see the trend of the line graph with counts and the month. We can see that in latter part of 2015 the user count tweeting started increasing and then we can see that in 2016 user count decreased and then in may it started increasing and slowing decreasing again. In 2017 we can see the users that tweeting is reducing

In [148]:
twitter_archive_master.columns
Out[148]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
       'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
       'jpg_url', 'img_num', 'first_predict_breed', 'prediction1_confidence',
       'predict1_result', 'second_predict_breed', 'prediction2_confidence',
       'predict2_result', 'third_predict_breed', 'prediction3_confidence',
       'predict3_result', 'favorite_count', 'retweet_count', 'year', 'month',
       'day', 'time', 'day_of_week'],
      dtype='object')
In [149]:
sot1=twitter_archive_master['dog_stages'].value_counts()
In [151]:
sot1
Out[151]:
pupper     212
doggo       74
puppo       23
floofer      8
Name: dog_stages, dtype: int64
In [156]:
colours = ["blue", "green", "yellow"]
sns.set(style='whitegrid')
sns.countplot(data=twitter_archive_master,x='dog_stages',order=sot1.index,palette=colours)
plt.xticks(rotation=45)
plt.xlabel("Count",fontsize=12)
plt.ylabel("Dog_Stage",fontsize=12)
plt.title("Dog_StageDistribution",fontsize=17)
plt.show()

Here,we can see the distribution of dog stages. It shows that ‘pupper’ (a small doggo, usually younger) is the most popular dog stage, followed by ‘doggo’ and ‘puppo’. It could be due to the young and unmatured dog is usually cuter than the adult dog. It should also be noticed that there’s huge amount missing data in dog stage, hence the distrubition might not be true.

Image prediction Data Analysis
In [166]:
sns.set(style='darkgrid')
sns.set(color_codes=True)
sns.distplot(twitter_archive_master['prediction1_confidence'],color='green')
plt.xlabel('Confidencelevel')
plt.ylabel('Frequency')
plt.title('Distrubutionplot')
Out[166]:
Text(0.5,1,'Distrubutionplot')
In [163]:
sort1=twitter_archive_master['predict1_result'].value_counts()
In [164]:
sort1
Out[164]:
True     1715
False     596
Name: predict1_result, dtype: int64
In [165]:
explode = (0.1,0)
fig1,ax1=plt.subplots()
ax1.pie(sort1,explode=explode,labels=sort1.index,autopct='%1.1f%%',shadow=True,startangle=60)
ax1.axis('equal')
plt.tight_layout()
plt.show()

The first plot above shows the prediction success rate of whether or not first prediction is a breed of dog. The pie chart indicates almost 74.2% situations the predictions are correct, even though this result is not good enough for a deep learning model. The second plot shows how confident the algorithm is in its first prediction. We can see 100% is the most cases, however the amounts of 0.1 to 0.8 dominate the entire distribution. That also could suggest that the model is not good enough

Refrences

In [ ]: